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1580 lines
49 KiB
1580 lines
49 KiB
import cython
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from cython import Py_ssize_t
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from cython cimport floating
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from libc.stdlib cimport (
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free,
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malloc,
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)
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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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complex64_t,
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complex128_t,
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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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intp_t,
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ndarray,
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uint8_t,
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uint16_t,
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uint32_t,
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uint64_t,
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)
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from numpy.math cimport NAN
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cnp.import_array()
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from pandas._libs.algos cimport kth_smallest_c
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from pandas._libs.util cimport get_nat
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from pandas._libs.algos import (
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ensure_platform_int,
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groupsort_indexer,
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rank_1d,
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take_2d_axis1_float64_float64,
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)
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from pandas._libs.dtypes cimport (
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iu_64_floating_obj_t,
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iu_64_floating_t,
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numeric_t,
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)
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from pandas._libs.missing cimport checknull
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cdef int64_t NPY_NAT = get_nat()
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_int64_max = np.iinfo(np.int64).max
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cdef float64_t NaN = <float64_t>np.NaN
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cdef enum InterpolationEnumType:
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INTERPOLATION_LINEAR,
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INTERPOLATION_LOWER,
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INTERPOLATION_HIGHER,
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INTERPOLATION_NEAREST,
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INTERPOLATION_MIDPOINT
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cdef inline float64_t median_linear(float64_t* a, int n) nogil:
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cdef:
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int i, j, na_count = 0
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float64_t result
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float64_t* tmp
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if n == 0:
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return NaN
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# count NAs
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for i in range(n):
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if a[i] != a[i]:
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na_count += 1
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if na_count:
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if na_count == n:
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return NaN
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tmp = <float64_t*>malloc((n - na_count) * sizeof(float64_t))
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j = 0
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for i in range(n):
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if a[i] == a[i]:
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tmp[j] = a[i]
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j += 1
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a = tmp
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n -= na_count
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if n % 2:
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result = kth_smallest_c(a, n // 2, n)
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else:
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result = (kth_smallest_c(a, n // 2, n) +
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kth_smallest_c(a, n // 2 - 1, n)) / 2
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if na_count:
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free(a)
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return result
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@cython.boundscheck(False)
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@cython.wraparound(False)
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def group_median_float64(ndarray[float64_t, ndim=2] out,
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ndarray[int64_t] counts,
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ndarray[float64_t, ndim=2] values,
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ndarray[intp_t] labels,
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Py_ssize_t min_count=-1) -> None:
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"""
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Only aggregates on axis=0
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"""
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cdef:
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Py_ssize_t i, j, N, K, ngroups, size
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ndarray[intp_t] _counts
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ndarray[float64_t, ndim=2] data
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ndarray[intp_t] indexer
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float64_t* ptr
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assert min_count == -1, "'min_count' only used in add and prod"
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ngroups = len(counts)
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N, K = (<object>values).shape
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indexer, _counts = groupsort_indexer(labels, ngroups)
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counts[:] = _counts[1:]
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data = np.empty((K, N), dtype=np.float64)
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ptr = <float64_t*>cnp.PyArray_DATA(data)
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take_2d_axis1_float64_float64(values.T, indexer, out=data)
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with nogil:
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for i in range(K):
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# exclude NA group
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ptr += _counts[0]
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for j in range(ngroups):
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size = _counts[j + 1]
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out[j, i] = median_linear(ptr, size)
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ptr += size
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@cython.boundscheck(False)
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@cython.wraparound(False)
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def group_cumprod_float64(float64_t[:, ::1] out,
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const float64_t[:, :] values,
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const intp_t[::1] labels,
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int ngroups,
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bint is_datetimelike,
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bint skipna=True) -> None:
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"""
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Cumulative product of columns of `values`, in row groups `labels`.
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Parameters
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----------
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out : np.ndarray[np.float64, ndim=2]
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Array to store cumprod in.
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values : np.ndarray[np.float64, ndim=2]
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Values to take cumprod of.
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labels : np.ndarray[np.intp]
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Labels to group by.
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ngroups : int
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Number of groups, larger than all entries of `labels`.
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is_datetimelike : bool
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Always false, `values` is never datetime-like.
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skipna : bool
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If true, ignore nans in `values`.
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Notes
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-----
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This method modifies the `out` parameter, rather than returning an object.
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"""
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cdef:
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Py_ssize_t i, j, N, K, size
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float64_t val
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float64_t[:, ::1] accum
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intp_t lab
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N, K = (<object>values).shape
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accum = np.ones((ngroups, K), dtype=np.float64)
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with nogil:
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for i in range(N):
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lab = labels[i]
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if lab < 0:
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continue
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for j in range(K):
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val = values[i, j]
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if val == val:
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accum[lab, j] *= val
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out[i, j] = accum[lab, j]
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else:
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out[i, j] = NaN
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if not skipna:
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accum[lab, j] = NaN
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break
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@cython.boundscheck(False)
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@cython.wraparound(False)
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def group_cumsum(numeric_t[:, ::1] out,
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ndarray[numeric_t, ndim=2] values,
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const intp_t[::1] labels,
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int ngroups,
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is_datetimelike,
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bint skipna=True) -> None:
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"""
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Cumulative sum of columns of `values`, in row groups `labels`.
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Parameters
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----------
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out : np.ndarray[ndim=2]
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Array to store cumsum in.
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values : np.ndarray[ndim=2]
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Values to take cumsum of.
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labels : np.ndarray[np.intp]
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Labels to group by.
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ngroups : int
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Number of groups, larger than all entries of `labels`.
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is_datetimelike : bool
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True if `values` contains datetime-like entries.
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skipna : bool
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If true, ignore nans in `values`.
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Notes
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-----
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This method modifies the `out` parameter, rather than returning an object.
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"""
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cdef:
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Py_ssize_t i, j, N, K, size
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numeric_t val, y, t
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numeric_t[:, ::1] accum, compensation
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intp_t lab
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N, K = (<object>values).shape
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accum = np.zeros((ngroups, K), dtype=np.asarray(values).dtype)
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compensation = np.zeros((ngroups, K), dtype=np.asarray(values).dtype)
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with nogil:
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for i in range(N):
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lab = labels[i]
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if lab < 0:
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continue
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for j in range(K):
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val = values[i, j]
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# For floats, use Kahan summation to reduce floating-point
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# error (https://en.wikipedia.org/wiki/Kahan_summation_algorithm)
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if numeric_t == float32_t or numeric_t == float64_t:
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if val == val:
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y = val - compensation[lab, j]
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t = accum[lab, j] + y
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compensation[lab, j] = t - accum[lab, j] - y
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accum[lab, j] = t
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out[i, j] = t
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else:
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out[i, j] = NaN
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if not skipna:
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accum[lab, j] = NaN
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break
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else:
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t = val + accum[lab, j]
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accum[lab, j] = t
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out[i, j] = t
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@cython.boundscheck(False)
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@cython.wraparound(False)
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def group_shift_indexer(int64_t[::1] out, const intp_t[::1] labels,
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int ngroups, int periods) -> None:
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cdef:
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Py_ssize_t N, i, j, ii, lab
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int offset = 0, sign
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int64_t idxer, idxer_slot
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int64_t[::1] label_seen = np.zeros(ngroups, dtype=np.int64)
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int64_t[:, ::1] label_indexer
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N, = (<object>labels).shape
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if periods < 0:
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periods = -periods
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offset = N - 1
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sign = -1
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elif periods > 0:
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offset = 0
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sign = 1
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if periods == 0:
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with nogil:
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for i in range(N):
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out[i] = i
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else:
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# array of each previous indexer seen
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label_indexer = np.zeros((ngroups, periods), dtype=np.int64)
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with nogil:
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for i in range(N):
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# reverse iterator if shifting backwards
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ii = offset + sign * i
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lab = labels[ii]
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# Skip null keys
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if lab == -1:
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out[ii] = -1
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continue
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label_seen[lab] += 1
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idxer_slot = label_seen[lab] % periods
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idxer = label_indexer[lab, idxer_slot]
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if label_seen[lab] > periods:
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out[ii] = idxer
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else:
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out[ii] = -1
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label_indexer[lab, idxer_slot] = ii
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def group_fillna_indexer(ndarray[intp_t] out, ndarray[intp_t] labels,
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ndarray[intp_t] sorted_labels,
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ndarray[uint8_t] mask, str direction,
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int64_t limit, bint dropna) -> None:
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"""
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Indexes how to fill values forwards or backwards within a group.
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Parameters
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----------
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out : np.ndarray[np.intp]
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Values into which this method will write its results.
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labels : np.ndarray[np.intp]
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Array containing unique label for each group, with its ordering
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matching up to the corresponding record in `values`.
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sorted_labels : np.ndarray[np.intp]
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obtained by `np.argsort(labels, kind="mergesort")`; reversed if
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direction == "bfill"
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values : np.ndarray[np.uint8]
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Containing the truth value of each element.
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mask : np.ndarray[np.uint8]
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Indicating whether a value is na or not.
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direction : {'ffill', 'bfill'}
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Direction for fill to be applied (forwards or backwards, respectively)
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limit : Consecutive values to fill before stopping, or -1 for no limit
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dropna : Flag to indicate if NaN groups should return all NaN values
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Notes
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-----
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This method modifies the `out` parameter rather than returning an object
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"""
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cdef:
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Py_ssize_t i, N, idx
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intp_t curr_fill_idx=-1
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int64_t filled_vals = 0
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N = len(out)
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# Make sure all arrays are the same size
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assert N == len(labels) == len(mask)
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with nogil:
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for i in range(N):
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idx = sorted_labels[i]
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if dropna and labels[idx] == -1: # nan-group gets nan-values
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curr_fill_idx = -1
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elif mask[idx] == 1: # is missing
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# Stop filling once we've hit the limit
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if filled_vals >= limit and limit != -1:
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curr_fill_idx = -1
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filled_vals += 1
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else: # reset items when not missing
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filled_vals = 0
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curr_fill_idx = idx
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out[idx] = curr_fill_idx
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# If we move to the next group, reset
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# the fill_idx and counter
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if i == N - 1 or labels[idx] != labels[sorted_labels[i + 1]]:
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curr_fill_idx = -1
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filled_vals = 0
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@cython.boundscheck(False)
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@cython.wraparound(False)
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def group_any_all(int8_t[:, ::1] out,
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const int8_t[:, :] values,
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const intp_t[::1] labels,
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const uint8_t[:, :] mask,
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str val_test,
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bint skipna,
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bint nullable) -> None:
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"""
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Aggregated boolean values to show truthfulness of group elements. If the
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input is a nullable type (nullable=True), the result will be computed
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using Kleene logic.
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Parameters
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----------
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out : np.ndarray[np.int8]
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Values into which this method will write its results.
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labels : np.ndarray[np.intp]
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Array containing unique label for each group, with its
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ordering matching up to the corresponding record in `values`
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values : np.ndarray[np.int8]
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Containing the truth value of each element.
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mask : np.ndarray[np.uint8]
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Indicating whether a value is na or not.
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val_test : {'any', 'all'}
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String object dictating whether to use any or all truth testing
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skipna : bool
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Flag to ignore nan values during truth testing
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nullable : bool
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Whether or not the input is a nullable type. If True, the
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result will be computed using Kleene logic
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Notes
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-----
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This method modifies the `out` parameter rather than returning an object.
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The returned values will either be 0, 1 (False or True, respectively), or
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-1 to signify a masked position in the case of a nullable input.
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"""
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cdef:
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Py_ssize_t i, j, N = len(labels), K = out.shape[1]
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intp_t lab
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int8_t flag_val, val
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if val_test == 'all':
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# Because the 'all' value of an empty iterable in Python is True we can
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# start with an array full of ones and set to zero when a False value
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# is encountered
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flag_val = 0
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elif val_test == 'any':
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# Because the 'any' value of an empty iterable in Python is False we
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# can start with an array full of zeros and set to one only if any
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# value encountered is True
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flag_val = 1
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else:
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raise ValueError("'bool_func' must be either 'any' or 'all'!")
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out[:] = 1 - flag_val
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with nogil:
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for i in range(N):
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lab = labels[i]
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if lab < 0:
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continue
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for j in range(K):
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if skipna and mask[i, j]:
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continue
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if nullable and mask[i, j]:
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# Set the position as masked if `out[lab] != flag_val`, which
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# would indicate True/False has not yet been seen for any/all,
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# so by Kleene logic the result is currently unknown
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if out[lab, j] != flag_val:
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out[lab, j] = -1
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continue
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|
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val = values[i, j]
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# If True and 'any' or False and 'all', the result is
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# already determined
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if val == flag_val:
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out[lab, j] = flag_val
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|
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# ----------------------------------------------------------------------
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# group_add, group_prod, group_var, group_mean, group_ohlc
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# ----------------------------------------------------------------------
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|
|
ctypedef fused add_t:
|
|
float64_t
|
|
float32_t
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|
complex64_t
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|
complex128_t
|
|
object
|
|
|
|
ctypedef fused mean_t:
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|
float64_t
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|
float32_t
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|
complex64_t
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|
complex128_t
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|
|
|
|
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@cython.wraparound(False)
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@cython.boundscheck(False)
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|
def group_add(add_t[:, ::1] out,
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int64_t[::1] counts,
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ndarray[add_t, ndim=2] values,
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const intp_t[::1] labels,
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Py_ssize_t min_count=0,
|
|
bint datetimelike=False) -> None:
|
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"""
|
|
Only aggregates on axis=0 using Kahan summation
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|
"""
|
|
cdef:
|
|
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
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add_t val, t, y
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add_t[:, ::1] sumx, compensation
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int64_t[:, ::1] nobs
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Py_ssize_t len_values = len(values), len_labels = len(labels)
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|
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if len_values != len_labels:
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raise ValueError("len(index) != len(labels)")
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|
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nobs = np.zeros((<object>out).shape, dtype=np.int64)
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# the below is equivalent to `np.zeros_like(out)` but faster
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sumx = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
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compensation = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
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|
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N, K = (<object>values).shape
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|
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if add_t is object:
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# NB: this does not use 'compensation' like the non-object track does.
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for i in range(N):
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lab = labels[i]
|
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if lab < 0:
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continue
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|
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counts[lab] += 1
|
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for j in range(K):
|
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val = values[i, j]
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|
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# not nan
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|
if not checknull(val):
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nobs[lab, j] += 1
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|
|
if nobs[lab, j] == 1:
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# i.e. we haven't added anything yet; avoid TypeError
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|
# if e.g. val is a str and sumx[lab, j] is 0
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|
t = val
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|
else:
|
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t = sumx[lab, j] + val
|
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sumx[lab, j] = t
|
|
|
|
for i in range(ncounts):
|
|
for j in range(K):
|
|
if nobs[i, j] < min_count:
|
|
out[i, j] = NAN
|
|
else:
|
|
out[i, j] = sumx[i, j]
|
|
else:
|
|
with nogil:
|
|
for i in range(N):
|
|
lab = labels[i]
|
|
if lab < 0:
|
|
continue
|
|
|
|
counts[lab] += 1
|
|
for j in range(K):
|
|
val = values[i, j]
|
|
|
|
# not nan
|
|
# With dt64/td64 values, values have been cast to float64
|
|
# instead if int64 for group_add, but the logic
|
|
# is otherwise the same as in _treat_as_na
|
|
if val == val and not (
|
|
add_t is float64_t
|
|
and datetimelike
|
|
and val == <float64_t>NPY_NAT
|
|
):
|
|
nobs[lab, j] += 1
|
|
y = val - compensation[lab, j]
|
|
t = sumx[lab, j] + y
|
|
compensation[lab, j] = t - sumx[lab, j] - y
|
|
sumx[lab, j] = t
|
|
|
|
for i in range(ncounts):
|
|
for j in range(K):
|
|
if nobs[i, j] < min_count:
|
|
out[i, j] = NAN
|
|
else:
|
|
out[i, j] = sumx[i, j]
|
|
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
def group_prod(floating[:, ::1] out,
|
|
int64_t[::1] counts,
|
|
ndarray[floating, ndim=2] values,
|
|
const intp_t[::1] labels,
|
|
Py_ssize_t min_count=0) -> None:
|
|
"""
|
|
Only aggregates on axis=0
|
|
"""
|
|
cdef:
|
|
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
|
|
floating val, count
|
|
floating[:, ::1] prodx
|
|
int64_t[:, ::1] nobs
|
|
Py_ssize_t len_values = len(values), len_labels = len(labels)
|
|
|
|
if len_values != len_labels:
|
|
raise ValueError("len(index) != len(labels)")
|
|
|
|
nobs = np.zeros((<object>out).shape, dtype=np.int64)
|
|
prodx = np.ones((<object>out).shape, dtype=(<object>out).base.dtype)
|
|
|
|
N, K = (<object>values).shape
|
|
|
|
with nogil:
|
|
for i in range(N):
|
|
lab = labels[i]
|
|
if lab < 0:
|
|
continue
|
|
|
|
counts[lab] += 1
|
|
for j in range(K):
|
|
val = values[i, j]
|
|
|
|
# not nan
|
|
if val == val:
|
|
nobs[lab, j] += 1
|
|
prodx[lab, j] *= val
|
|
|
|
for i in range(ncounts):
|
|
for j in range(K):
|
|
if nobs[i, j] < min_count:
|
|
out[i, j] = NAN
|
|
else:
|
|
out[i, j] = prodx[i, j]
|
|
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
@cython.cdivision(True)
|
|
def group_var(floating[:, ::1] out,
|
|
int64_t[::1] counts,
|
|
ndarray[floating, ndim=2] values,
|
|
const intp_t[::1] labels,
|
|
Py_ssize_t min_count=-1,
|
|
int64_t ddof=1) -> None:
|
|
cdef:
|
|
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
|
|
floating val, ct, oldmean
|
|
floating[:, ::1] mean
|
|
int64_t[:, ::1] nobs
|
|
Py_ssize_t len_values = len(values), len_labels = len(labels)
|
|
|
|
assert min_count == -1, "'min_count' only used in add and prod"
|
|
|
|
if len_values != len_labels:
|
|
raise ValueError("len(index) != len(labels)")
|
|
|
|
nobs = np.zeros((<object>out).shape, dtype=np.int64)
|
|
mean = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
|
|
|
|
N, K = (<object>values).shape
|
|
|
|
out[:, :] = 0.0
|
|
|
|
with nogil:
|
|
for i in range(N):
|
|
lab = labels[i]
|
|
if lab < 0:
|
|
continue
|
|
|
|
counts[lab] += 1
|
|
|
|
for j in range(K):
|
|
val = values[i, j]
|
|
|
|
# not nan
|
|
if val == val:
|
|
nobs[lab, j] += 1
|
|
oldmean = mean[lab, j]
|
|
mean[lab, j] += (val - oldmean) / nobs[lab, j]
|
|
out[lab, j] += (val - mean[lab, j]) * (val - oldmean)
|
|
|
|
for i in range(ncounts):
|
|
for j in range(K):
|
|
ct = nobs[i, j]
|
|
if ct <= ddof:
|
|
out[i, j] = NAN
|
|
else:
|
|
out[i, j] /= (ct - ddof)
|
|
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
def group_mean(mean_t[:, ::1] out,
|
|
int64_t[::1] counts,
|
|
ndarray[mean_t, ndim=2] values,
|
|
const intp_t[::1] labels,
|
|
Py_ssize_t min_count=-1,
|
|
bint is_datetimelike=False,
|
|
const uint8_t[:, ::1] mask=None,
|
|
uint8_t[:, ::1] result_mask=None
|
|
) -> None:
|
|
"""
|
|
Compute the mean per label given a label assignment for each value.
|
|
NaN values are ignored.
|
|
|
|
Parameters
|
|
----------
|
|
out : np.ndarray[floating]
|
|
Values into which this method will write its results.
|
|
counts : np.ndarray[int64]
|
|
A zeroed array of the same shape as labels,
|
|
populated by group sizes during algorithm.
|
|
values : np.ndarray[floating]
|
|
2-d array of the values to find the mean of.
|
|
labels : np.ndarray[np.intp]
|
|
Array containing unique label for each group, with its
|
|
ordering matching up to the corresponding record in `values`.
|
|
min_count : Py_ssize_t
|
|
Only used in add and prod. Always -1.
|
|
is_datetimelike : bool
|
|
True if `values` contains datetime-like entries.
|
|
mask : ndarray[bool, ndim=2], optional
|
|
Not used.
|
|
result_mask : ndarray[bool, ndim=2], optional
|
|
Not used.
|
|
|
|
Notes
|
|
-----
|
|
This method modifies the `out` parameter rather than returning an object.
|
|
`counts` is modified to hold group sizes
|
|
"""
|
|
|
|
cdef:
|
|
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
|
|
mean_t val, count, y, t, nan_val
|
|
mean_t[:, ::1] sumx, compensation
|
|
int64_t[:, ::1] nobs
|
|
Py_ssize_t len_values = len(values), len_labels = len(labels)
|
|
|
|
assert min_count == -1, "'min_count' only used in add and prod"
|
|
|
|
if len_values != len_labels:
|
|
raise ValueError("len(index) != len(labels)")
|
|
|
|
# the below is equivalent to `np.zeros_like(out)` but faster
|
|
nobs = np.zeros((<object>out).shape, dtype=np.int64)
|
|
sumx = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
|
|
compensation = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
|
|
|
|
N, K = (<object>values).shape
|
|
nan_val = NPY_NAT if is_datetimelike else NAN
|
|
|
|
with nogil:
|
|
for i in range(N):
|
|
lab = labels[i]
|
|
if lab < 0:
|
|
continue
|
|
|
|
counts[lab] += 1
|
|
for j in range(K):
|
|
val = values[i, j]
|
|
# not nan
|
|
if val == val and not (is_datetimelike and val == NPY_NAT):
|
|
nobs[lab, j] += 1
|
|
y = val - compensation[lab, j]
|
|
t = sumx[lab, j] + y
|
|
compensation[lab, j] = t - sumx[lab, j] - y
|
|
sumx[lab, j] = t
|
|
|
|
for i in range(ncounts):
|
|
for j in range(K):
|
|
count = nobs[i, j]
|
|
if nobs[i, j] == 0:
|
|
out[i, j] = nan_val
|
|
else:
|
|
out[i, j] = sumx[i, j] / count
|
|
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
def group_ohlc(floating[:, ::1] out,
|
|
int64_t[::1] counts,
|
|
ndarray[floating, ndim=2] values,
|
|
const intp_t[::1] labels,
|
|
Py_ssize_t min_count=-1) -> None:
|
|
"""
|
|
Only aggregates on axis=0
|
|
"""
|
|
cdef:
|
|
Py_ssize_t i, j, N, K, lab
|
|
floating val
|
|
|
|
assert min_count == -1, "'min_count' only used in add and prod"
|
|
|
|
if len(labels) == 0:
|
|
return
|
|
|
|
N, K = (<object>values).shape
|
|
|
|
if out.shape[1] != 4:
|
|
raise ValueError('Output array must have 4 columns')
|
|
|
|
if K > 1:
|
|
raise NotImplementedError("Argument 'values' must have only one dimension")
|
|
out[:] = np.nan
|
|
|
|
with nogil:
|
|
for i in range(N):
|
|
lab = labels[i]
|
|
if lab == -1:
|
|
continue
|
|
|
|
counts[lab] += 1
|
|
val = values[i, 0]
|
|
if val != val:
|
|
continue
|
|
|
|
if out[lab, 0] != out[lab, 0]:
|
|
out[lab, 0] = out[lab, 1] = out[lab, 2] = out[lab, 3] = val
|
|
else:
|
|
out[lab, 1] = max(out[lab, 1], val)
|
|
out[lab, 2] = min(out[lab, 2], val)
|
|
out[lab, 3] = val
|
|
|
|
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
def group_quantile(ndarray[float64_t, ndim=2] out,
|
|
ndarray[numeric_t, ndim=1] values,
|
|
ndarray[intp_t] labels,
|
|
ndarray[uint8_t] mask,
|
|
const intp_t[:] sort_indexer,
|
|
const float64_t[:] qs,
|
|
str interpolation) -> None:
|
|
"""
|
|
Calculate the quantile per group.
|
|
|
|
Parameters
|
|
----------
|
|
out : np.ndarray[np.float64, ndim=2]
|
|
Array of aggregated values that will be written to.
|
|
values : np.ndarray
|
|
Array containing the values to apply the function against.
|
|
labels : ndarray[np.intp]
|
|
Array containing the unique group labels.
|
|
sort_indexer : ndarray[np.intp]
|
|
Indices describing sort order by values and labels.
|
|
qs : ndarray[float64_t]
|
|
The quantile values to search for.
|
|
interpolation : {'linear', 'lower', 'highest', 'nearest', 'midpoint'}
|
|
|
|
Notes
|
|
-----
|
|
Rather than explicitly returning a value, this function modifies the
|
|
provided `out` parameter.
|
|
"""
|
|
cdef:
|
|
Py_ssize_t i, N=len(labels), ngroups, grp_sz, non_na_sz, k, nqs
|
|
Py_ssize_t grp_start=0, idx=0
|
|
intp_t lab
|
|
InterpolationEnumType interp
|
|
float64_t q_val, q_idx, frac, val, next_val
|
|
int64_t[::1] counts, non_na_counts
|
|
|
|
assert values.shape[0] == N
|
|
|
|
if any(not (0 <= q <= 1) for q in qs):
|
|
wrong = [x for x in qs if not (0 <= x <= 1)][0]
|
|
raise ValueError(
|
|
f"Each 'q' must be between 0 and 1. Got '{wrong}' instead"
|
|
)
|
|
|
|
inter_methods = {
|
|
'linear': INTERPOLATION_LINEAR,
|
|
'lower': INTERPOLATION_LOWER,
|
|
'higher': INTERPOLATION_HIGHER,
|
|
'nearest': INTERPOLATION_NEAREST,
|
|
'midpoint': INTERPOLATION_MIDPOINT,
|
|
}
|
|
interp = inter_methods[interpolation]
|
|
|
|
nqs = len(qs)
|
|
ngroups = len(out)
|
|
counts = np.zeros(ngroups, dtype=np.int64)
|
|
non_na_counts = np.zeros(ngroups, dtype=np.int64)
|
|
|
|
# First figure out the size of every group
|
|
with nogil:
|
|
for i in range(N):
|
|
lab = labels[i]
|
|
if lab == -1: # NA group label
|
|
continue
|
|
|
|
counts[lab] += 1
|
|
if not mask[i]:
|
|
non_na_counts[lab] += 1
|
|
|
|
with nogil:
|
|
for i in range(ngroups):
|
|
# Figure out how many group elements there are
|
|
grp_sz = counts[i]
|
|
non_na_sz = non_na_counts[i]
|
|
|
|
if non_na_sz == 0:
|
|
for k in range(nqs):
|
|
out[i, k] = NaN
|
|
else:
|
|
for k in range(nqs):
|
|
q_val = qs[k]
|
|
|
|
# Calculate where to retrieve the desired value
|
|
# Casting to int will intentionally truncate result
|
|
idx = grp_start + <int64_t>(q_val * <float64_t>(non_na_sz - 1))
|
|
|
|
val = values[sort_indexer[idx]]
|
|
# If requested quantile falls evenly on a particular index
|
|
# then write that index's value out. Otherwise interpolate
|
|
q_idx = q_val * (non_na_sz - 1)
|
|
frac = q_idx % 1
|
|
|
|
if frac == 0.0 or interp == INTERPOLATION_LOWER:
|
|
out[i, k] = val
|
|
else:
|
|
next_val = values[sort_indexer[idx + 1]]
|
|
if interp == INTERPOLATION_LINEAR:
|
|
out[i, k] = val + (next_val - val) * frac
|
|
elif interp == INTERPOLATION_HIGHER:
|
|
out[i, k] = next_val
|
|
elif interp == INTERPOLATION_MIDPOINT:
|
|
out[i, k] = (val + next_val) / 2.0
|
|
elif interp == INTERPOLATION_NEAREST:
|
|
if frac > .5 or (frac == .5 and q_val > .5): # Always OK?
|
|
out[i, k] = next_val
|
|
else:
|
|
out[i, k] = val
|
|
|
|
# Increment the index reference in sorted_arr for the next group
|
|
grp_start += grp_sz
|
|
|
|
|
|
# ----------------------------------------------------------------------
|
|
# group_nth, group_last, group_rank
|
|
# ----------------------------------------------------------------------
|
|
|
|
cdef inline bint _treat_as_na(iu_64_floating_obj_t val, bint is_datetimelike) nogil:
|
|
if iu_64_floating_obj_t is object:
|
|
# Should never be used, but we need to avoid the `val != val` below
|
|
# or else cython will raise about gil acquisition.
|
|
raise NotImplementedError
|
|
|
|
elif iu_64_floating_obj_t is int64_t:
|
|
return is_datetimelike and val == NPY_NAT
|
|
elif iu_64_floating_obj_t is uint64_t:
|
|
# There is no NA value for uint64
|
|
return False
|
|
else:
|
|
return val != val
|
|
|
|
|
|
# GH#31710 use memorviews once cython 0.30 is released so we can
|
|
# use `const iu_64_floating_obj_t[:, :] values`
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
def group_last(iu_64_floating_obj_t[:, ::1] out,
|
|
int64_t[::1] counts,
|
|
ndarray[iu_64_floating_obj_t, ndim=2] values,
|
|
const intp_t[::1] labels,
|
|
Py_ssize_t min_count=-1) -> None:
|
|
"""
|
|
Only aggregates on axis=0
|
|
"""
|
|
cdef:
|
|
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
|
|
iu_64_floating_obj_t val
|
|
ndarray[iu_64_floating_obj_t, ndim=2] resx
|
|
ndarray[int64_t, ndim=2] nobs
|
|
bint runtime_error = False
|
|
|
|
# TODO(cython3):
|
|
# Instead of `labels.shape[0]` use `len(labels)`
|
|
if not len(values) == labels.shape[0]:
|
|
raise AssertionError("len(index) != len(labels)")
|
|
|
|
min_count = max(min_count, 1)
|
|
nobs = np.zeros((<object>out).shape, dtype=np.int64)
|
|
if iu_64_floating_obj_t is object:
|
|
resx = np.empty((<object>out).shape, dtype=object)
|
|
else:
|
|
resx = np.empty_like(out)
|
|
|
|
N, K = (<object>values).shape
|
|
|
|
if iu_64_floating_obj_t is object:
|
|
# TODO(cython3): De-duplicate once conditional-nogil is available
|
|
for i in range(N):
|
|
lab = labels[i]
|
|
if lab < 0:
|
|
continue
|
|
|
|
counts[lab] += 1
|
|
for j in range(K):
|
|
val = values[i, j]
|
|
|
|
if not checknull(val):
|
|
# NB: use _treat_as_na here once
|
|
# conditional-nogil is available.
|
|
nobs[lab, j] += 1
|
|
resx[lab, j] = val
|
|
|
|
for i in range(ncounts):
|
|
for j in range(K):
|
|
if nobs[i, j] < min_count:
|
|
out[i, j] = None
|
|
else:
|
|
out[i, j] = resx[i, j]
|
|
else:
|
|
with nogil:
|
|
for i in range(N):
|
|
lab = labels[i]
|
|
if lab < 0:
|
|
continue
|
|
|
|
counts[lab] += 1
|
|
for j in range(K):
|
|
val = values[i, j]
|
|
|
|
if not _treat_as_na(val, True):
|
|
# TODO: Sure we always want is_datetimelike=True?
|
|
nobs[lab, j] += 1
|
|
resx[lab, j] = val
|
|
|
|
for i in range(ncounts):
|
|
for j in range(K):
|
|
if nobs[i, j] < min_count:
|
|
if iu_64_floating_obj_t is int64_t:
|
|
out[i, j] = NPY_NAT
|
|
elif iu_64_floating_obj_t is uint64_t:
|
|
runtime_error = True
|
|
break
|
|
else:
|
|
out[i, j] = NAN
|
|
|
|
else:
|
|
out[i, j] = resx[i, j]
|
|
|
|
if runtime_error:
|
|
# We cannot raise directly above because that is within a nogil
|
|
# block.
|
|
raise RuntimeError("empty group with uint64_t")
|
|
|
|
|
|
# GH#31710 use memorviews once cython 0.30 is released so we can
|
|
# use `const iu_64_floating_obj_t[:, :] values`
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
def group_nth(iu_64_floating_obj_t[:, ::1] out,
|
|
int64_t[::1] counts,
|
|
ndarray[iu_64_floating_obj_t, ndim=2] values,
|
|
const intp_t[::1] labels,
|
|
int64_t min_count=-1,
|
|
int64_t rank=1,
|
|
) -> None:
|
|
"""
|
|
Only aggregates on axis=0
|
|
"""
|
|
cdef:
|
|
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
|
|
iu_64_floating_obj_t val
|
|
ndarray[iu_64_floating_obj_t, ndim=2] resx
|
|
ndarray[int64_t, ndim=2] nobs
|
|
bint runtime_error = False
|
|
|
|
# TODO(cython3):
|
|
# Instead of `labels.shape[0]` use `len(labels)`
|
|
if not len(values) == labels.shape[0]:
|
|
raise AssertionError("len(index) != len(labels)")
|
|
|
|
min_count = max(min_count, 1)
|
|
nobs = np.zeros((<object>out).shape, dtype=np.int64)
|
|
if iu_64_floating_obj_t is object:
|
|
resx = np.empty((<object>out).shape, dtype=object)
|
|
else:
|
|
resx = np.empty_like(out)
|
|
|
|
N, K = (<object>values).shape
|
|
|
|
if iu_64_floating_obj_t is object:
|
|
# TODO(cython3): De-duplicate once conditional-nogil is available
|
|
for i in range(N):
|
|
lab = labels[i]
|
|
if lab < 0:
|
|
continue
|
|
|
|
counts[lab] += 1
|
|
for j in range(K):
|
|
val = values[i, j]
|
|
|
|
if not checknull(val):
|
|
# NB: use _treat_as_na here once
|
|
# conditional-nogil is available.
|
|
nobs[lab, j] += 1
|
|
if nobs[lab, j] == rank:
|
|
resx[lab, j] = val
|
|
|
|
for i in range(ncounts):
|
|
for j in range(K):
|
|
if nobs[i, j] < min_count:
|
|
out[i, j] = None
|
|
else:
|
|
out[i, j] = resx[i, j]
|
|
|
|
else:
|
|
with nogil:
|
|
for i in range(N):
|
|
lab = labels[i]
|
|
if lab < 0:
|
|
continue
|
|
|
|
counts[lab] += 1
|
|
for j in range(K):
|
|
val = values[i, j]
|
|
|
|
if not _treat_as_na(val, True):
|
|
# TODO: Sure we always want is_datetimelike=True?
|
|
nobs[lab, j] += 1
|
|
if nobs[lab, j] == rank:
|
|
resx[lab, j] = val
|
|
|
|
for i in range(ncounts):
|
|
for j in range(K):
|
|
if nobs[i, j] < min_count:
|
|
if iu_64_floating_obj_t is int64_t:
|
|
out[i, j] = NPY_NAT
|
|
elif iu_64_floating_obj_t is uint64_t:
|
|
runtime_error = True
|
|
break
|
|
else:
|
|
out[i, j] = NAN
|
|
else:
|
|
out[i, j] = resx[i, j]
|
|
|
|
if runtime_error:
|
|
# We cannot raise directly above because that is within a nogil
|
|
# block.
|
|
raise RuntimeError("empty group with uint64_t")
|
|
|
|
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
def group_rank(float64_t[:, ::1] out,
|
|
ndarray[iu_64_floating_obj_t, ndim=2] values,
|
|
const intp_t[::1] labels,
|
|
int ngroups,
|
|
bint is_datetimelike, str ties_method="average",
|
|
bint ascending=True, bint pct=False, str na_option="keep") -> None:
|
|
"""
|
|
Provides the rank of values within each group.
|
|
|
|
Parameters
|
|
----------
|
|
out : np.ndarray[np.float64, ndim=2]
|
|
Values to which this method will write its results.
|
|
values : np.ndarray of iu_64_floating_obj_t values to be ranked
|
|
labels : np.ndarray[np.intp]
|
|
Array containing unique label for each group, with its ordering
|
|
matching up to the corresponding record in `values`
|
|
ngroups : int
|
|
This parameter is not used, is needed to match signatures of other
|
|
groupby functions.
|
|
is_datetimelike : bool
|
|
True if `values` contains datetime-like entries.
|
|
ties_method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
|
|
* average: average rank of group
|
|
* min: lowest rank in group
|
|
* max: highest rank in group
|
|
* first: ranks assigned in order they appear in the array
|
|
* dense: like 'min', but rank always increases by 1 between groups
|
|
ascending : bool, default True
|
|
False for ranks by high (1) to low (N)
|
|
na_option : {'keep', 'top', 'bottom'}, default 'keep'
|
|
pct : bool, default False
|
|
Compute percentage rank of data within each group
|
|
na_option : {'keep', 'top', 'bottom'}, default 'keep'
|
|
* keep: leave NA values where they are
|
|
* top: smallest rank if ascending
|
|
* bottom: smallest rank if descending
|
|
|
|
Notes
|
|
-----
|
|
This method modifies the `out` parameter rather than returning an object
|
|
"""
|
|
cdef:
|
|
Py_ssize_t i, k, N
|
|
ndarray[float64_t, ndim=1] result
|
|
|
|
N = values.shape[1]
|
|
|
|
for k in range(N):
|
|
result = rank_1d(
|
|
values=values[:, k],
|
|
labels=labels,
|
|
is_datetimelike=is_datetimelike,
|
|
ties_method=ties_method,
|
|
ascending=ascending,
|
|
pct=pct,
|
|
na_option=na_option
|
|
)
|
|
for i in range(len(result)):
|
|
# TODO: why can't we do out[:, k] = result?
|
|
out[i, k] = result[i]
|
|
|
|
|
|
# ----------------------------------------------------------------------
|
|
# group_min, group_max
|
|
# ----------------------------------------------------------------------
|
|
|
|
# TODO: consider implementing for more dtypes
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
cdef group_min_max(iu_64_floating_t[:, ::1] out,
|
|
int64_t[::1] counts,
|
|
ndarray[iu_64_floating_t, ndim=2] values,
|
|
const intp_t[::1] labels,
|
|
Py_ssize_t min_count=-1,
|
|
bint is_datetimelike=False,
|
|
bint compute_max=True,
|
|
const uint8_t[:, ::1] mask=None,
|
|
uint8_t[:, ::1] result_mask=None):
|
|
"""
|
|
Compute minimum/maximum of columns of `values`, in row groups `labels`.
|
|
|
|
Parameters
|
|
----------
|
|
out : np.ndarray[iu_64_floating_t, ndim=2]
|
|
Array to store result in.
|
|
counts : np.ndarray[int64]
|
|
Input as a zeroed array, populated by group sizes during algorithm
|
|
values : array
|
|
Values to find column-wise min/max of.
|
|
labels : np.ndarray[np.intp]
|
|
Labels to group by.
|
|
min_count : Py_ssize_t, default -1
|
|
The minimum number of non-NA group elements, NA result if threshold
|
|
is not met
|
|
is_datetimelike : bool
|
|
True if `values` contains datetime-like entries.
|
|
compute_max : bint, default True
|
|
True to compute group-wise max, False to compute min
|
|
mask : ndarray[bool, ndim=2], optional
|
|
If not None, indices represent missing values,
|
|
otherwise the mask will not be used
|
|
result_mask : ndarray[bool, ndim=2], optional
|
|
If not None, these specify locations in the output that are NA.
|
|
Modified in-place.
|
|
|
|
Notes
|
|
-----
|
|
This method modifies the `out` parameter, rather than returning an object.
|
|
`counts` is modified to hold group sizes
|
|
"""
|
|
cdef:
|
|
Py_ssize_t i, j, N, K, lab, ngroups = len(counts)
|
|
iu_64_floating_t val, nan_val
|
|
ndarray[iu_64_floating_t, ndim=2] group_min_or_max
|
|
bint runtime_error = False
|
|
int64_t[:, ::1] nobs
|
|
bint uses_mask = mask is not None
|
|
bint isna_entry
|
|
|
|
# TODO(cython3):
|
|
# Instead of `labels.shape[0]` use `len(labels)`
|
|
if not len(values) == labels.shape[0]:
|
|
raise AssertionError("len(index) != len(labels)")
|
|
|
|
min_count = max(min_count, 1)
|
|
nobs = np.zeros((<object>out).shape, dtype=np.int64)
|
|
|
|
group_min_or_max = np.empty_like(out)
|
|
if iu_64_floating_t is int64_t:
|
|
group_min_or_max[:] = -_int64_max if compute_max else _int64_max
|
|
nan_val = NPY_NAT
|
|
elif iu_64_floating_t is uint64_t:
|
|
# NB: We do not define nan_val because there is no such thing
|
|
# for uint64_t. We carefully avoid having to reference it in this
|
|
# case.
|
|
group_min_or_max[:] = 0 if compute_max else np.iinfo(np.uint64).max
|
|
else:
|
|
group_min_or_max[:] = -np.inf if compute_max else np.inf
|
|
nan_val = NAN
|
|
|
|
N, K = (<object>values).shape
|
|
|
|
with nogil:
|
|
for i in range(N):
|
|
lab = labels[i]
|
|
if lab < 0:
|
|
continue
|
|
|
|
counts[lab] += 1
|
|
for j in range(K):
|
|
val = values[i, j]
|
|
|
|
if uses_mask:
|
|
isna_entry = mask[i, j]
|
|
else:
|
|
isna_entry = _treat_as_na(val, is_datetimelike)
|
|
|
|
if not isna_entry:
|
|
nobs[lab, j] += 1
|
|
if compute_max:
|
|
if val > group_min_or_max[lab, j]:
|
|
group_min_or_max[lab, j] = val
|
|
else:
|
|
if val < group_min_or_max[lab, j]:
|
|
group_min_or_max[lab, j] = val
|
|
|
|
for i in range(ngroups):
|
|
for j in range(K):
|
|
if nobs[i, j] < min_count:
|
|
if iu_64_floating_t is uint64_t:
|
|
runtime_error = True
|
|
break
|
|
else:
|
|
if uses_mask:
|
|
result_mask[i, j] = True
|
|
else:
|
|
out[i, j] = nan_val
|
|
else:
|
|
out[i, j] = group_min_or_max[i, j]
|
|
|
|
if runtime_error:
|
|
# We cannot raise directly above because that is within a nogil
|
|
# block.
|
|
raise RuntimeError("empty group with uint64_t")
|
|
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
def group_max(iu_64_floating_t[:, ::1] out,
|
|
int64_t[::1] counts,
|
|
ndarray[iu_64_floating_t, ndim=2] values,
|
|
const intp_t[::1] labels,
|
|
Py_ssize_t min_count=-1,
|
|
bint is_datetimelike=False,
|
|
const uint8_t[:, ::1] mask=None,
|
|
uint8_t[:, ::1] result_mask=None) -> None:
|
|
"""See group_min_max.__doc__"""
|
|
group_min_max(
|
|
out,
|
|
counts,
|
|
values,
|
|
labels,
|
|
min_count=min_count,
|
|
is_datetimelike=is_datetimelike,
|
|
compute_max=True,
|
|
mask=mask,
|
|
result_mask=result_mask,
|
|
)
|
|
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
def group_min(iu_64_floating_t[:, ::1] out,
|
|
int64_t[::1] counts,
|
|
ndarray[iu_64_floating_t, ndim=2] values,
|
|
const intp_t[::1] labels,
|
|
Py_ssize_t min_count=-1,
|
|
bint is_datetimelike=False,
|
|
const uint8_t[:, ::1] mask=None,
|
|
uint8_t[:, ::1] result_mask=None) -> None:
|
|
"""See group_min_max.__doc__"""
|
|
group_min_max(
|
|
out,
|
|
counts,
|
|
values,
|
|
labels,
|
|
min_count=min_count,
|
|
is_datetimelike=is_datetimelike,
|
|
compute_max=False,
|
|
mask=mask,
|
|
result_mask=result_mask,
|
|
)
|
|
|
|
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
cdef group_cummin_max(iu_64_floating_t[:, ::1] out,
|
|
ndarray[iu_64_floating_t, ndim=2] values,
|
|
uint8_t[:, ::1] mask,
|
|
const intp_t[::1] labels,
|
|
int ngroups,
|
|
bint is_datetimelike,
|
|
bint skipna,
|
|
bint compute_max):
|
|
"""
|
|
Cumulative minimum/maximum of columns of `values`, in row groups `labels`.
|
|
|
|
Parameters
|
|
----------
|
|
out : np.ndarray[iu_64_floating_t, ndim=2]
|
|
Array to store cummin/max in.
|
|
values : np.ndarray[iu_64_floating_t, ndim=2]
|
|
Values to take cummin/max of.
|
|
mask : np.ndarray[bool] or None
|
|
If not None, indices represent missing values,
|
|
otherwise the mask will not be used
|
|
labels : np.ndarray[np.intp]
|
|
Labels to group by.
|
|
ngroups : int
|
|
Number of groups, larger than all entries of `labels`.
|
|
is_datetimelike : bool
|
|
True if `values` contains datetime-like entries.
|
|
skipna : bool
|
|
If True, ignore nans in `values`.
|
|
compute_max : bool
|
|
True if cumulative maximum should be computed, False
|
|
if cumulative minimum should be computed
|
|
|
|
Notes
|
|
-----
|
|
This method modifies the `out` parameter, rather than returning an object.
|
|
"""
|
|
cdef:
|
|
iu_64_floating_t[:, ::1] accum
|
|
|
|
accum = np.empty((ngroups, (<object>values).shape[1]), dtype=values.dtype)
|
|
if iu_64_floating_t is int64_t:
|
|
accum[:] = -_int64_max if compute_max else _int64_max
|
|
elif iu_64_floating_t is uint64_t:
|
|
accum[:] = 0 if compute_max else np.iinfo(np.uint64).max
|
|
else:
|
|
accum[:] = -np.inf if compute_max else np.inf
|
|
|
|
if mask is not None:
|
|
masked_cummin_max(out, values, mask, labels, accum, skipna, compute_max)
|
|
else:
|
|
cummin_max(out, values, labels, accum, skipna, is_datetimelike, compute_max)
|
|
|
|
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
cdef cummin_max(iu_64_floating_t[:, ::1] out,
|
|
ndarray[iu_64_floating_t, ndim=2] values,
|
|
const intp_t[::1] labels,
|
|
iu_64_floating_t[:, ::1] accum,
|
|
bint skipna,
|
|
bint is_datetimelike,
|
|
bint compute_max):
|
|
"""
|
|
Compute the cumulative minimum/maximum of columns of `values`, in row groups
|
|
`labels`.
|
|
"""
|
|
cdef:
|
|
Py_ssize_t i, j, N, K
|
|
iu_64_floating_t val, mval, na_val
|
|
uint8_t[:, ::1] seen_na
|
|
intp_t lab
|
|
bint na_possible
|
|
|
|
if iu_64_floating_t is float64_t or iu_64_floating_t is float32_t:
|
|
na_val = NaN
|
|
na_possible = True
|
|
elif is_datetimelike:
|
|
na_val = NPY_NAT
|
|
na_possible = True
|
|
# Will never be used, just to avoid uninitialized warning
|
|
else:
|
|
na_val = 0
|
|
na_possible = False
|
|
|
|
if na_possible:
|
|
seen_na = np.zeros((<object>accum).shape, dtype=np.uint8)
|
|
|
|
N, K = (<object>values).shape
|
|
with nogil:
|
|
for i in range(N):
|
|
lab = labels[i]
|
|
if lab < 0:
|
|
continue
|
|
for j in range(K):
|
|
if not skipna and na_possible and seen_na[lab, j]:
|
|
out[i, j] = na_val
|
|
else:
|
|
val = values[i, j]
|
|
if not _treat_as_na(val, is_datetimelike):
|
|
mval = accum[lab, j]
|
|
if compute_max:
|
|
if val > mval:
|
|
accum[lab, j] = mval = val
|
|
else:
|
|
if val < mval:
|
|
accum[lab, j] = mval = val
|
|
out[i, j] = mval
|
|
else:
|
|
seen_na[lab, j] = 1
|
|
out[i, j] = val
|
|
|
|
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
cdef masked_cummin_max(iu_64_floating_t[:, ::1] out,
|
|
ndarray[iu_64_floating_t, ndim=2] values,
|
|
uint8_t[:, ::1] mask,
|
|
const intp_t[::1] labels,
|
|
iu_64_floating_t[:, ::1] accum,
|
|
bint skipna,
|
|
bint compute_max):
|
|
"""
|
|
Compute the cumulative minimum/maximum of columns of `values`, in row groups
|
|
`labels` with a masked algorithm.
|
|
"""
|
|
cdef:
|
|
Py_ssize_t i, j, N, K
|
|
iu_64_floating_t val, mval
|
|
uint8_t[:, ::1] seen_na
|
|
intp_t lab
|
|
|
|
N, K = (<object>values).shape
|
|
seen_na = np.zeros((<object>accum).shape, dtype=np.uint8)
|
|
with nogil:
|
|
for i in range(N):
|
|
lab = labels[i]
|
|
if lab < 0:
|
|
continue
|
|
for j in range(K):
|
|
if not skipna and seen_na[lab, j]:
|
|
mask[i, j] = 1
|
|
else:
|
|
if not mask[i, j]:
|
|
val = values[i, j]
|
|
mval = accum[lab, j]
|
|
if compute_max:
|
|
if val > mval:
|
|
accum[lab, j] = mval = val
|
|
else:
|
|
if val < mval:
|
|
accum[lab, j] = mval = val
|
|
out[i, j] = mval
|
|
else:
|
|
seen_na[lab, j] = 1
|
|
|
|
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
def group_cummin(iu_64_floating_t[:, ::1] out,
|
|
ndarray[iu_64_floating_t, ndim=2] values,
|
|
const intp_t[::1] labels,
|
|
int ngroups,
|
|
bint is_datetimelike,
|
|
uint8_t[:, ::1] mask=None,
|
|
bint skipna=True) -> None:
|
|
"""See group_cummin_max.__doc__"""
|
|
group_cummin_max(
|
|
out,
|
|
values,
|
|
mask,
|
|
labels,
|
|
ngroups,
|
|
is_datetimelike,
|
|
skipna,
|
|
compute_max=False
|
|
)
|
|
|
|
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
def group_cummax(iu_64_floating_t[:, ::1] out,
|
|
ndarray[iu_64_floating_t, ndim=2] values,
|
|
const intp_t[::1] labels,
|
|
int ngroups,
|
|
bint is_datetimelike,
|
|
uint8_t[:, ::1] mask=None,
|
|
bint skipna=True) -> None:
|
|
"""See group_cummin_max.__doc__"""
|
|
group_cummin_max(
|
|
out,
|
|
values,
|
|
mask,
|
|
labels,
|
|
ngroups,
|
|
is_datetimelike,
|
|
skipna,
|
|
compute_max=True
|
|
)
|
|
|