1537 lines
50 KiB
Cython
1537 lines
50 KiB
Cython
|
cimport cython
|
||
|
|
from cython cimport Py_ssize_t
|
||
|
|
from libc.math cimport (
|
||
|
|
fabs,
|
||
|
|
sqrt,
|
||
|
|
)
|
||
|
|
from libc.stdlib cimport (
|
||
|
|
free,
|
||
|
|
malloc,
|
||
|
|
)
|
||
|
|
from libc.string cimport memmove
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
cimport numpy as cnp
|
||
|
|
from numpy cimport (
|
||
|
|
NPY_FLOAT64,
|
||
|
|
NPY_INT8,
|
||
|
|
NPY_INT16,
|
||
|
|
NPY_INT32,
|
||
|
|
NPY_INT64,
|
||
|
|
NPY_OBJECT,
|
||
|
|
NPY_UINT64,
|
||
|
|
float32_t,
|
||
|
|
float64_t,
|
||
|
|
int8_t,
|
||
|
|
int16_t,
|
||
|
|
int32_t,
|
||
|
|
int64_t,
|
||
|
|
intp_t,
|
||
|
|
ndarray,
|
||
|
|
uint8_t,
|
||
|
|
uint16_t,
|
||
|
|
uint32_t,
|
||
|
|
uint64_t,
|
||
|
|
)
|
||
|
|
|
||
|
|
cnp.import_array()
|
||
|
|
|
||
|
|
cimport pandas._libs.util as util
|
||
|
|
from pandas._libs.dtypes cimport (
|
||
|
|
numeric_object_t,
|
||
|
|
numeric_t,
|
||
|
|
)
|
||
|
|
from pandas._libs.khash cimport (
|
||
|
|
kh_destroy_int64,
|
||
|
|
kh_get_int64,
|
||
|
|
kh_init_int64,
|
||
|
|
kh_int64_t,
|
||
|
|
kh_put_int64,
|
||
|
|
kh_resize_int64,
|
||
|
|
khiter_t,
|
||
|
|
)
|
||
|
|
from pandas._libs.util cimport get_nat
|
||
|
|
|
||
|
|
import pandas._libs.missing as missing
|
||
|
|
|
||
|
|
cdef:
|
||
|
|
float64_t FP_ERR = 1e-13
|
||
|
|
float64_t NaN = <float64_t>np.NaN
|
||
|
|
int64_t NPY_NAT = get_nat()
|
||
|
|
|
||
|
|
|
||
|
|
tiebreakers = {
|
||
|
|
"average": TIEBREAK_AVERAGE,
|
||
|
|
"min": TIEBREAK_MIN,
|
||
|
|
"max": TIEBREAK_MAX,
|
||
|
|
"first": TIEBREAK_FIRST,
|
||
|
|
"dense": TIEBREAK_DENSE,
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
cdef bint are_diff(object left, object right):
|
||
|
|
try:
|
||
|
|
return fabs(left - right) > FP_ERR
|
||
|
|
except TypeError:
|
||
|
|
return left != right
|
||
|
|
|
||
|
|
|
||
|
|
class Infinity:
|
||
|
|
"""
|
||
|
|
Provide a positive Infinity comparison method for ranking.
|
||
|
|
"""
|
||
|
|
def __lt__(self, other):
|
||
|
|
return False
|
||
|
|
|
||
|
|
def __le__(self, other):
|
||
|
|
return isinstance(other, Infinity)
|
||
|
|
|
||
|
|
def __eq__(self, other):
|
||
|
|
return isinstance(other, Infinity)
|
||
|
|
|
||
|
|
def __ne__(self, other):
|
||
|
|
return not isinstance(other, Infinity)
|
||
|
|
|
||
|
|
def __gt__(self, other):
|
||
|
|
return (not isinstance(other, Infinity) and
|
||
|
|
not missing.checknull(other))
|
||
|
|
|
||
|
|
def __ge__(self, other):
|
||
|
|
return not missing.checknull(other)
|
||
|
|
|
||
|
|
|
||
|
|
class NegInfinity:
|
||
|
|
"""
|
||
|
|
Provide a negative Infinity comparison method for ranking.
|
||
|
|
"""
|
||
|
|
def __lt__(self, other):
|
||
|
|
return (not isinstance(other, NegInfinity) and
|
||
|
|
not missing.checknull(other))
|
||
|
|
|
||
|
|
def __le__(self, other):
|
||
|
|
return not missing.checknull(other)
|
||
|
|
|
||
|
|
def __eq__(self, other):
|
||
|
|
return isinstance(other, NegInfinity)
|
||
|
|
|
||
|
|
def __ne__(self, other):
|
||
|
|
return not isinstance(other, NegInfinity)
|
||
|
|
|
||
|
|
def __gt__(self, other):
|
||
|
|
return False
|
||
|
|
|
||
|
|
def __ge__(self, other):
|
||
|
|
return isinstance(other, NegInfinity)
|
||
|
|
|
||
|
|
|
||
|
|
@cython.wraparound(False)
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
cpdef ndarray[int64_t, ndim=1] unique_deltas(const int64_t[:] arr):
|
||
|
|
"""
|
||
|
|
Efficiently find the unique first-differences of the given array.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
arr : ndarray[int64_t]
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
ndarray[int64_t]
|
||
|
|
An ordered ndarray[int64_t]
|
||
|
|
"""
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, n = len(arr)
|
||
|
|
int64_t val
|
||
|
|
khiter_t k
|
||
|
|
kh_int64_t *table
|
||
|
|
int ret = 0
|
||
|
|
list uniques = []
|
||
|
|
ndarray[int64_t, ndim=1] result
|
||
|
|
|
||
|
|
table = kh_init_int64()
|
||
|
|
kh_resize_int64(table, 10)
|
||
|
|
for i in range(n - 1):
|
||
|
|
val = arr[i + 1] - arr[i]
|
||
|
|
k = kh_get_int64(table, val)
|
||
|
|
if k == table.n_buckets:
|
||
|
|
kh_put_int64(table, val, &ret)
|
||
|
|
uniques.append(val)
|
||
|
|
kh_destroy_int64(table)
|
||
|
|
|
||
|
|
result = np.array(uniques, dtype=np.int64)
|
||
|
|
result.sort()
|
||
|
|
return result
|
||
|
|
|
||
|
|
|
||
|
|
@cython.wraparound(False)
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
def is_lexsorted(list_of_arrays: list) -> bool:
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i
|
||
|
|
Py_ssize_t n, nlevels
|
||
|
|
int64_t k, cur, pre
|
||
|
|
ndarray arr
|
||
|
|
bint result = True
|
||
|
|
|
||
|
|
nlevels = len(list_of_arrays)
|
||
|
|
n = len(list_of_arrays[0])
|
||
|
|
|
||
|
|
cdef int64_t **vecs = <int64_t**>malloc(nlevels * sizeof(int64_t*))
|
||
|
|
for i in range(nlevels):
|
||
|
|
arr = list_of_arrays[i]
|
||
|
|
assert arr.dtype.name == "int64"
|
||
|
|
vecs[i] = <int64_t*>cnp.PyArray_DATA(arr)
|
||
|
|
|
||
|
|
# Assume uniqueness??
|
||
|
|
with nogil:
|
||
|
|
for i in range(1, n):
|
||
|
|
for k in range(nlevels):
|
||
|
|
cur = vecs[k][i]
|
||
|
|
pre = vecs[k][i -1]
|
||
|
|
if cur == pre:
|
||
|
|
continue
|
||
|
|
elif cur > pre:
|
||
|
|
break
|
||
|
|
else:
|
||
|
|
result = False
|
||
|
|
break
|
||
|
|
if not result:
|
||
|
|
break
|
||
|
|
free(vecs)
|
||
|
|
return result
|
||
|
|
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
@cython.wraparound(False)
|
||
|
|
def groupsort_indexer(const intp_t[:] index, Py_ssize_t ngroups):
|
||
|
|
"""
|
||
|
|
Compute a 1-d indexer.
|
||
|
|
|
||
|
|
The indexer is an ordering of the passed index,
|
||
|
|
ordered by the groups.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
index: np.ndarray[np.intp]
|
||
|
|
Mappings from group -> position.
|
||
|
|
ngroups: int64
|
||
|
|
Number of groups.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
ndarray[intp_t, ndim=1]
|
||
|
|
Indexer
|
||
|
|
ndarray[intp_t, ndim=1]
|
||
|
|
Group Counts
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
This is a reverse of the label factorization process.
|
||
|
|
"""
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, label, n
|
||
|
|
intp_t[::1] indexer, where, counts
|
||
|
|
|
||
|
|
counts = np.zeros(ngroups + 1, dtype=np.intp)
|
||
|
|
n = len(index)
|
||
|
|
indexer = np.zeros(n, dtype=np.intp)
|
||
|
|
where = np.zeros(ngroups + 1, dtype=np.intp)
|
||
|
|
|
||
|
|
with nogil:
|
||
|
|
|
||
|
|
# count group sizes, location 0 for NA
|
||
|
|
for i in range(n):
|
||
|
|
counts[index[i] + 1] += 1
|
||
|
|
|
||
|
|
# mark the start of each contiguous group of like-indexed data
|
||
|
|
for i in range(1, ngroups + 1):
|
||
|
|
where[i] = where[i - 1] + counts[i - 1]
|
||
|
|
|
||
|
|
# this is our indexer
|
||
|
|
for i in range(n):
|
||
|
|
label = index[i] + 1
|
||
|
|
indexer[where[label]] = i
|
||
|
|
where[label] += 1
|
||
|
|
|
||
|
|
return indexer.base, counts.base
|
||
|
|
|
||
|
|
|
||
|
|
cdef Py_ssize_t swap(numeric_t *a, numeric_t *b) nogil:
|
||
|
|
cdef:
|
||
|
|
numeric_t t
|
||
|
|
|
||
|
|
# cython doesn't allow pointer dereference so use array syntax
|
||
|
|
t = a[0]
|
||
|
|
a[0] = b[0]
|
||
|
|
b[0] = t
|
||
|
|
return 0
|
||
|
|
|
||
|
|
|
||
|
|
cdef numeric_t kth_smallest_c(numeric_t* arr, Py_ssize_t k, Py_ssize_t n) nogil:
|
||
|
|
"""
|
||
|
|
See kth_smallest.__doc__. The additional parameter n specifies the maximum
|
||
|
|
number of elements considered in arr, needed for compatibility with usage
|
||
|
|
in groupby.pyx
|
||
|
|
"""
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, j, left, m
|
||
|
|
numeric_t x
|
||
|
|
|
||
|
|
left = 0
|
||
|
|
m = n - 1
|
||
|
|
|
||
|
|
while left < m:
|
||
|
|
x = arr[k]
|
||
|
|
i = left
|
||
|
|
j = m
|
||
|
|
|
||
|
|
while 1:
|
||
|
|
while arr[i] < x:
|
||
|
|
i += 1
|
||
|
|
while x < arr[j]:
|
||
|
|
j -= 1
|
||
|
|
if i <= j:
|
||
|
|
swap(&arr[i], &arr[j])
|
||
|
|
i += 1
|
||
|
|
j -= 1
|
||
|
|
|
||
|
|
if i > j:
|
||
|
|
break
|
||
|
|
|
||
|
|
if j < k:
|
||
|
|
left = i
|
||
|
|
if k < i:
|
||
|
|
m = j
|
||
|
|
return arr[k]
|
||
|
|
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
@cython.wraparound(False)
|
||
|
|
def kth_smallest(numeric_t[::1] arr, Py_ssize_t k) -> numeric_t:
|
||
|
|
"""
|
||
|
|
Compute the kth smallest value in arr. Note that the input
|
||
|
|
array will be modified.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
arr : numeric[::1]
|
||
|
|
Array to compute the kth smallest value for, must be
|
||
|
|
contiguous
|
||
|
|
k : Py_ssize_t
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
numeric
|
||
|
|
The kth smallest value in arr
|
||
|
|
"""
|
||
|
|
cdef:
|
||
|
|
numeric_t result
|
||
|
|
|
||
|
|
with nogil:
|
||
|
|
result = kth_smallest_c(&arr[0], k, arr.shape[0])
|
||
|
|
|
||
|
|
return result
|
||
|
|
|
||
|
|
|
||
|
|
# ----------------------------------------------------------------------
|
||
|
|
# Pairwise correlation/covariance
|
||
|
|
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
@cython.wraparound(False)
|
||
|
|
@cython.cdivision(True)
|
||
|
|
def nancorr(const float64_t[:, :] mat, bint cov=False, minp=None):
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, xi, yi, N, K
|
||
|
|
bint minpv
|
||
|
|
float64_t[:, ::1] result
|
||
|
|
ndarray[uint8_t, ndim=2] mask
|
||
|
|
int64_t nobs = 0
|
||
|
|
float64_t vx, vy, dx, dy, meanx, meany, divisor, ssqdmx, ssqdmy, covxy
|
||
|
|
|
||
|
|
N, K = (<object>mat).shape
|
||
|
|
|
||
|
|
if minp is None:
|
||
|
|
minpv = 1
|
||
|
|
else:
|
||
|
|
minpv = <int>minp
|
||
|
|
|
||
|
|
result = np.empty((K, K), dtype=np.float64)
|
||
|
|
mask = np.isfinite(mat).view(np.uint8)
|
||
|
|
|
||
|
|
with nogil:
|
||
|
|
for xi in range(K):
|
||
|
|
for yi in range(xi + 1):
|
||
|
|
# Welford's method for the variance-calculation
|
||
|
|
# https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
|
||
|
|
nobs = ssqdmx = ssqdmy = covxy = meanx = meany = 0
|
||
|
|
for i in range(N):
|
||
|
|
if mask[i, xi] and mask[i, yi]:
|
||
|
|
vx = mat[i, xi]
|
||
|
|
vy = mat[i, yi]
|
||
|
|
nobs += 1
|
||
|
|
dx = vx - meanx
|
||
|
|
dy = vy - meany
|
||
|
|
meanx += 1. / nobs * dx
|
||
|
|
meany += 1. / nobs * dy
|
||
|
|
ssqdmx += (vx - meanx) * dx
|
||
|
|
ssqdmy += (vy - meany) * dy
|
||
|
|
covxy += (vx - meanx) * dy
|
||
|
|
|
||
|
|
if nobs < minpv:
|
||
|
|
result[xi, yi] = result[yi, xi] = NaN
|
||
|
|
else:
|
||
|
|
divisor = (nobs - 1.0) if cov else sqrt(ssqdmx * ssqdmy)
|
||
|
|
|
||
|
|
if divisor != 0:
|
||
|
|
result[xi, yi] = result[yi, xi] = covxy / divisor
|
||
|
|
else:
|
||
|
|
result[xi, yi] = result[yi, xi] = NaN
|
||
|
|
|
||
|
|
return result.base
|
||
|
|
|
||
|
|
# ----------------------------------------------------------------------
|
||
|
|
# Pairwise Spearman correlation
|
||
|
|
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
@cython.wraparound(False)
|
||
|
|
def nancorr_spearman(ndarray[float64_t, ndim=2] mat, Py_ssize_t minp=1) -> ndarray:
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, xi, yi, N, K
|
||
|
|
ndarray[float64_t, ndim=2] result
|
||
|
|
ndarray[float64_t, ndim=2] ranked_mat
|
||
|
|
ndarray[float64_t, ndim=1] rankedx, rankedy
|
||
|
|
float64_t[::1] maskedx, maskedy
|
||
|
|
ndarray[uint8_t, ndim=2] mask
|
||
|
|
int64_t nobs = 0
|
||
|
|
bint no_nans
|
||
|
|
float64_t vx, vy, sumx, sumxx, sumyy, mean, divisor
|
||
|
|
|
||
|
|
N, K = (<object>mat).shape
|
||
|
|
|
||
|
|
# Handle the edge case where we know all results will be nan
|
||
|
|
# to keep conditional logic inside loop simpler
|
||
|
|
if N < minp:
|
||
|
|
result = np.full((K, K), np.nan, dtype=np.float64)
|
||
|
|
return result
|
||
|
|
|
||
|
|
result = np.empty((K, K), dtype=np.float64)
|
||
|
|
mask = np.isfinite(mat).view(np.uint8)
|
||
|
|
no_nans = mask.all()
|
||
|
|
|
||
|
|
ranked_mat = np.empty((N, K), dtype=np.float64)
|
||
|
|
|
||
|
|
# Note: we index into maskedx, maskedy in loops up to nobs, but using N is safe
|
||
|
|
# here since N >= nobs and values are stored contiguously
|
||
|
|
maskedx = np.empty(N, dtype=np.float64)
|
||
|
|
maskedy = np.empty(N, dtype=np.float64)
|
||
|
|
for i in range(K):
|
||
|
|
ranked_mat[:, i] = rank_1d(mat[:, i])
|
||
|
|
|
||
|
|
with nogil:
|
||
|
|
for xi in range(K):
|
||
|
|
for yi in range(xi + 1):
|
||
|
|
sumx = sumxx = sumyy = 0
|
||
|
|
|
||
|
|
# Fastpath for data with no nans/infs, allows avoiding mask checks
|
||
|
|
# and array reassignments
|
||
|
|
if no_nans:
|
||
|
|
mean = (N + 1) / 2.
|
||
|
|
|
||
|
|
# now the cov numerator
|
||
|
|
for i in range(N):
|
||
|
|
vx = ranked_mat[i, xi] - mean
|
||
|
|
vy = ranked_mat[i, yi] - mean
|
||
|
|
|
||
|
|
sumx += vx * vy
|
||
|
|
sumxx += vx * vx
|
||
|
|
sumyy += vy * vy
|
||
|
|
else:
|
||
|
|
nobs = 0
|
||
|
|
# Keep track of whether we need to recompute ranks
|
||
|
|
all_ranks = True
|
||
|
|
for i in range(N):
|
||
|
|
all_ranks &= not (mask[i, xi] ^ mask[i, yi])
|
||
|
|
if mask[i, xi] and mask[i, yi]:
|
||
|
|
maskedx[nobs] = ranked_mat[i, xi]
|
||
|
|
maskedy[nobs] = ranked_mat[i, yi]
|
||
|
|
nobs += 1
|
||
|
|
|
||
|
|
if nobs < minp:
|
||
|
|
result[xi, yi] = result[yi, xi] = NaN
|
||
|
|
continue
|
||
|
|
else:
|
||
|
|
if not all_ranks:
|
||
|
|
with gil:
|
||
|
|
# We need to slice back to nobs because rank_1d will
|
||
|
|
# require arrays of nobs length
|
||
|
|
rankedx = rank_1d(np.asarray(maskedx)[:nobs])
|
||
|
|
rankedy = rank_1d(np.asarray(maskedy)[:nobs])
|
||
|
|
for i in range(nobs):
|
||
|
|
maskedx[i] = rankedx[i]
|
||
|
|
maskedy[i] = rankedy[i]
|
||
|
|
|
||
|
|
mean = (nobs + 1) / 2.
|
||
|
|
|
||
|
|
# now the cov numerator
|
||
|
|
for i in range(nobs):
|
||
|
|
vx = maskedx[i] - mean
|
||
|
|
vy = maskedy[i] - mean
|
||
|
|
|
||
|
|
sumx += vx * vy
|
||
|
|
sumxx += vx * vx
|
||
|
|
sumyy += vy * vy
|
||
|
|
|
||
|
|
divisor = sqrt(sumxx * sumyy)
|
||
|
|
|
||
|
|
if divisor != 0:
|
||
|
|
result[xi, yi] = result[yi, xi] = sumx / divisor
|
||
|
|
else:
|
||
|
|
result[xi, yi] = result[yi, xi] = NaN
|
||
|
|
|
||
|
|
return result
|
||
|
|
|
||
|
|
|
||
|
|
# ----------------------------------------------------------------------
|
||
|
|
|
||
|
|
def validate_limit(nobs: int | None, limit=None) -> int:
|
||
|
|
"""
|
||
|
|
Check that the `limit` argument is a positive integer.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
nobs : int
|
||
|
|
limit : object
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
int
|
||
|
|
The limit.
|
||
|
|
"""
|
||
|
|
if limit is None:
|
||
|
|
lim = nobs
|
||
|
|
else:
|
||
|
|
if not util.is_integer_object(limit):
|
||
|
|
raise ValueError("Limit must be an integer")
|
||
|
|
if limit < 1:
|
||
|
|
raise ValueError("Limit must be greater than 0")
|
||
|
|
lim = limit
|
||
|
|
|
||
|
|
return lim
|
||
|
|
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
@cython.wraparound(False)
|
||
|
|
def pad(
|
||
|
|
ndarray[numeric_object_t] old,
|
||
|
|
ndarray[numeric_object_t] new,
|
||
|
|
limit=None
|
||
|
|
) -> ndarray:
|
||
|
|
# -> ndarray[intp_t, ndim=1]
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, j, nleft, nright
|
||
|
|
ndarray[intp_t, ndim=1] indexer
|
||
|
|
numeric_object_t cur, next_val
|
||
|
|
int lim, fill_count = 0
|
||
|
|
|
||
|
|
nleft = len(old)
|
||
|
|
nright = len(new)
|
||
|
|
indexer = np.empty(nright, dtype=np.intp)
|
||
|
|
indexer[:] = -1
|
||
|
|
|
||
|
|
lim = validate_limit(nright, limit)
|
||
|
|
|
||
|
|
if nleft == 0 or nright == 0 or new[nright - 1] < old[0]:
|
||
|
|
return indexer
|
||
|
|
|
||
|
|
i = j = 0
|
||
|
|
|
||
|
|
cur = old[0]
|
||
|
|
|
||
|
|
while j <= nright - 1 and new[j] < cur:
|
||
|
|
j += 1
|
||
|
|
|
||
|
|
while True:
|
||
|
|
if j == nright:
|
||
|
|
break
|
||
|
|
|
||
|
|
if i == nleft - 1:
|
||
|
|
while j < nright:
|
||
|
|
if new[j] == cur:
|
||
|
|
indexer[j] = i
|
||
|
|
elif new[j] > cur and fill_count < lim:
|
||
|
|
indexer[j] = i
|
||
|
|
fill_count += 1
|
||
|
|
j += 1
|
||
|
|
break
|
||
|
|
|
||
|
|
next_val = old[i + 1]
|
||
|
|
|
||
|
|
while j < nright and cur <= new[j] < next_val:
|
||
|
|
if new[j] == cur:
|
||
|
|
indexer[j] = i
|
||
|
|
elif fill_count < lim:
|
||
|
|
indexer[j] = i
|
||
|
|
fill_count += 1
|
||
|
|
j += 1
|
||
|
|
|
||
|
|
fill_count = 0
|
||
|
|
i += 1
|
||
|
|
cur = next_val
|
||
|
|
|
||
|
|
return indexer
|
||
|
|
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
@cython.wraparound(False)
|
||
|
|
def pad_inplace(numeric_object_t[:] values, uint8_t[:] mask, limit=None):
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, N
|
||
|
|
numeric_object_t val
|
||
|
|
uint8_t prev_mask
|
||
|
|
int lim, fill_count = 0
|
||
|
|
|
||
|
|
N = len(values)
|
||
|
|
|
||
|
|
# GH#2778
|
||
|
|
if N == 0:
|
||
|
|
return
|
||
|
|
|
||
|
|
lim = validate_limit(N, limit)
|
||
|
|
|
||
|
|
val = values[0]
|
||
|
|
prev_mask = mask[0]
|
||
|
|
for i in range(N):
|
||
|
|
if mask[i]:
|
||
|
|
if fill_count >= lim:
|
||
|
|
continue
|
||
|
|
fill_count += 1
|
||
|
|
values[i] = val
|
||
|
|
mask[i] = prev_mask
|
||
|
|
else:
|
||
|
|
fill_count = 0
|
||
|
|
val = values[i]
|
||
|
|
prev_mask = mask[i]
|
||
|
|
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
@cython.wraparound(False)
|
||
|
|
def pad_2d_inplace(numeric_object_t[:, :] values, uint8_t[:, :] mask, limit=None):
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, j, N, K
|
||
|
|
numeric_object_t val
|
||
|
|
int lim, fill_count = 0
|
||
|
|
|
||
|
|
K, N = (<object>values).shape
|
||
|
|
|
||
|
|
# GH#2778
|
||
|
|
if N == 0:
|
||
|
|
return
|
||
|
|
|
||
|
|
lim = validate_limit(N, limit)
|
||
|
|
|
||
|
|
for j in range(K):
|
||
|
|
fill_count = 0
|
||
|
|
val = values[j, 0]
|
||
|
|
for i in range(N):
|
||
|
|
if mask[j, i]:
|
||
|
|
if fill_count >= lim or i == 0:
|
||
|
|
continue
|
||
|
|
fill_count += 1
|
||
|
|
values[j, i] = val
|
||
|
|
mask[j, i] = False
|
||
|
|
else:
|
||
|
|
fill_count = 0
|
||
|
|
val = values[j, i]
|
||
|
|
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
@cython.wraparound(False)
|
||
|
|
def backfill(
|
||
|
|
ndarray[numeric_object_t] old,
|
||
|
|
ndarray[numeric_object_t] new,
|
||
|
|
limit=None
|
||
|
|
) -> ndarray: # -> ndarray[intp_t, ndim=1]
|
||
|
|
"""
|
||
|
|
Backfilling logic for generating fill vector
|
||
|
|
|
||
|
|
Diagram of what's going on
|
||
|
|
|
||
|
|
Old New Fill vector Mask
|
||
|
|
. 0 1
|
||
|
|
. 0 1
|
||
|
|
. 0 1
|
||
|
|
A A 0 1
|
||
|
|
. 1 1
|
||
|
|
. 1 1
|
||
|
|
. 1 1
|
||
|
|
. 1 1
|
||
|
|
. 1 1
|
||
|
|
B B 1 1
|
||
|
|
. 2 1
|
||
|
|
. 2 1
|
||
|
|
. 2 1
|
||
|
|
C C 2 1
|
||
|
|
. 0
|
||
|
|
. 0
|
||
|
|
D
|
||
|
|
"""
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, j, nleft, nright
|
||
|
|
ndarray[intp_t, ndim=1] indexer
|
||
|
|
numeric_object_t cur, prev
|
||
|
|
int lim, fill_count = 0
|
||
|
|
|
||
|
|
nleft = len(old)
|
||
|
|
nright = len(new)
|
||
|
|
indexer = np.empty(nright, dtype=np.intp)
|
||
|
|
indexer[:] = -1
|
||
|
|
|
||
|
|
lim = validate_limit(nright, limit)
|
||
|
|
|
||
|
|
if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]:
|
||
|
|
return indexer
|
||
|
|
|
||
|
|
i = nleft - 1
|
||
|
|
j = nright - 1
|
||
|
|
|
||
|
|
cur = old[nleft - 1]
|
||
|
|
|
||
|
|
while j >= 0 and new[j] > cur:
|
||
|
|
j -= 1
|
||
|
|
|
||
|
|
while True:
|
||
|
|
if j < 0:
|
||
|
|
break
|
||
|
|
|
||
|
|
if i == 0:
|
||
|
|
while j >= 0:
|
||
|
|
if new[j] == cur:
|
||
|
|
indexer[j] = i
|
||
|
|
elif new[j] < cur and fill_count < lim:
|
||
|
|
indexer[j] = i
|
||
|
|
fill_count += 1
|
||
|
|
j -= 1
|
||
|
|
break
|
||
|
|
|
||
|
|
prev = old[i - 1]
|
||
|
|
|
||
|
|
while j >= 0 and prev < new[j] <= cur:
|
||
|
|
if new[j] == cur:
|
||
|
|
indexer[j] = i
|
||
|
|
elif new[j] < cur and fill_count < lim:
|
||
|
|
indexer[j] = i
|
||
|
|
fill_count += 1
|
||
|
|
j -= 1
|
||
|
|
|
||
|
|
fill_count = 0
|
||
|
|
i -= 1
|
||
|
|
cur = prev
|
||
|
|
|
||
|
|
return indexer
|
||
|
|
|
||
|
|
|
||
|
|
def backfill_inplace(numeric_object_t[:] values, uint8_t[:] mask, limit=None):
|
||
|
|
pad_inplace(values[::-1], mask[::-1], limit=limit)
|
||
|
|
|
||
|
|
|
||
|
|
def backfill_2d_inplace(numeric_object_t[:, :] values,
|
||
|
|
uint8_t[:, :] mask,
|
||
|
|
limit=None):
|
||
|
|
pad_2d_inplace(values[:, ::-1], mask[:, ::-1], limit)
|
||
|
|
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
@cython.wraparound(False)
|
||
|
|
def is_monotonic(ndarray[numeric_object_t, ndim=1] arr, bint timelike):
|
||
|
|
"""
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
tuple
|
||
|
|
is_monotonic_inc : bool
|
||
|
|
is_monotonic_dec : bool
|
||
|
|
is_unique : bool
|
||
|
|
"""
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, n
|
||
|
|
numeric_object_t prev, cur
|
||
|
|
bint is_monotonic_inc = 1
|
||
|
|
bint is_monotonic_dec = 1
|
||
|
|
bint is_unique = 1
|
||
|
|
bint is_strict_monotonic = 1
|
||
|
|
|
||
|
|
n = len(arr)
|
||
|
|
|
||
|
|
if n == 1:
|
||
|
|
if arr[0] != arr[0] or (numeric_object_t is int64_t and timelike and
|
||
|
|
arr[0] == NPY_NAT):
|
||
|
|
# single value is NaN
|
||
|
|
return False, False, True
|
||
|
|
else:
|
||
|
|
return True, True, True
|
||
|
|
elif n < 2:
|
||
|
|
return True, True, True
|
||
|
|
|
||
|
|
if timelike and <int64_t>arr[0] == NPY_NAT:
|
||
|
|
return False, False, True
|
||
|
|
|
||
|
|
if numeric_object_t is not object:
|
||
|
|
with nogil:
|
||
|
|
prev = arr[0]
|
||
|
|
for i in range(1, n):
|
||
|
|
cur = arr[i]
|
||
|
|
if timelike and <int64_t>cur == NPY_NAT:
|
||
|
|
is_monotonic_inc = 0
|
||
|
|
is_monotonic_dec = 0
|
||
|
|
break
|
||
|
|
if cur < prev:
|
||
|
|
is_monotonic_inc = 0
|
||
|
|
elif cur > prev:
|
||
|
|
is_monotonic_dec = 0
|
||
|
|
elif cur == prev:
|
||
|
|
is_unique = 0
|
||
|
|
else:
|
||
|
|
# cur or prev is NaN
|
||
|
|
is_monotonic_inc = 0
|
||
|
|
is_monotonic_dec = 0
|
||
|
|
break
|
||
|
|
if not is_monotonic_inc and not is_monotonic_dec:
|
||
|
|
is_monotonic_inc = 0
|
||
|
|
is_monotonic_dec = 0
|
||
|
|
break
|
||
|
|
prev = cur
|
||
|
|
else:
|
||
|
|
# object-dtype, identical to above except we cannot use `with nogil`
|
||
|
|
prev = arr[0]
|
||
|
|
for i in range(1, n):
|
||
|
|
cur = arr[i]
|
||
|
|
if timelike and <int64_t>cur == NPY_NAT:
|
||
|
|
is_monotonic_inc = 0
|
||
|
|
is_monotonic_dec = 0
|
||
|
|
break
|
||
|
|
if cur < prev:
|
||
|
|
is_monotonic_inc = 0
|
||
|
|
elif cur > prev:
|
||
|
|
is_monotonic_dec = 0
|
||
|
|
elif cur == prev:
|
||
|
|
is_unique = 0
|
||
|
|
else:
|
||
|
|
# cur or prev is NaN
|
||
|
|
is_monotonic_inc = 0
|
||
|
|
is_monotonic_dec = 0
|
||
|
|
break
|
||
|
|
if not is_monotonic_inc and not is_monotonic_dec:
|
||
|
|
is_monotonic_inc = 0
|
||
|
|
is_monotonic_dec = 0
|
||
|
|
break
|
||
|
|
prev = cur
|
||
|
|
|
||
|
|
is_strict_monotonic = is_unique and (is_monotonic_inc or is_monotonic_dec)
|
||
|
|
return is_monotonic_inc, is_monotonic_dec, is_strict_monotonic
|
||
|
|
|
||
|
|
|
||
|
|
# ----------------------------------------------------------------------
|
||
|
|
# rank_1d, rank_2d
|
||
|
|
# ----------------------------------------------------------------------
|
||
|
|
|
||
|
|
cdef numeric_object_t get_rank_nan_fill_val(
|
||
|
|
bint rank_nans_highest,
|
||
|
|
numeric_object_t val,
|
||
|
|
bint is_datetimelike=False,
|
||
|
|
):
|
||
|
|
"""
|
||
|
|
Return the value we'll use to represent missing values when sorting depending
|
||
|
|
on if we'd like missing values to end up at the top/bottom. (The second parameter
|
||
|
|
is unused, but needed for fused type specialization)
|
||
|
|
"""
|
||
|
|
if numeric_object_t is int64_t and is_datetimelike and not rank_nans_highest:
|
||
|
|
return NPY_NAT + 1
|
||
|
|
|
||
|
|
if rank_nans_highest:
|
||
|
|
if numeric_object_t is object:
|
||
|
|
return Infinity()
|
||
|
|
elif numeric_object_t is int64_t:
|
||
|
|
return util.INT64_MAX
|
||
|
|
elif numeric_object_t is int32_t:
|
||
|
|
return util.INT32_MAX
|
||
|
|
elif numeric_object_t is int16_t:
|
||
|
|
return util.INT16_MAX
|
||
|
|
elif numeric_object_t is int8_t:
|
||
|
|
return util.INT8_MAX
|
||
|
|
elif numeric_object_t is uint64_t:
|
||
|
|
return util.UINT64_MAX
|
||
|
|
elif numeric_object_t is uint32_t:
|
||
|
|
return util.UINT32_MAX
|
||
|
|
elif numeric_object_t is uint16_t:
|
||
|
|
return util.UINT16_MAX
|
||
|
|
elif numeric_object_t is uint8_t:
|
||
|
|
return util.UINT8_MAX
|
||
|
|
else:
|
||
|
|
return np.inf
|
||
|
|
else:
|
||
|
|
if numeric_object_t is object:
|
||
|
|
return NegInfinity()
|
||
|
|
elif numeric_object_t is int64_t:
|
||
|
|
# Note(jbrockmendel) 2022-03-15 for reasons unknown, using util.INT64_MIN
|
||
|
|
# instead of NPY_NAT here causes build warnings and failure in
|
||
|
|
# test_cummax_i8_at_implementation_bound
|
||
|
|
return NPY_NAT
|
||
|
|
elif numeric_object_t is int32_t:
|
||
|
|
return util.INT32_MIN
|
||
|
|
elif numeric_object_t is int16_t:
|
||
|
|
return util.INT16_MIN
|
||
|
|
elif numeric_object_t is int8_t:
|
||
|
|
return util.INT8_MIN
|
||
|
|
elif numeric_object_t is uint64_t:
|
||
|
|
return 0
|
||
|
|
elif numeric_object_t is uint32_t:
|
||
|
|
return 0
|
||
|
|
elif numeric_object_t is uint16_t:
|
||
|
|
return 0
|
||
|
|
elif numeric_object_t is uint8_t:
|
||
|
|
return 0
|
||
|
|
else:
|
||
|
|
return -np.inf
|
||
|
|
|
||
|
|
|
||
|
|
@cython.wraparound(False)
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
def rank_1d(
|
||
|
|
ndarray[numeric_object_t, ndim=1] values,
|
||
|
|
const intp_t[:] labels=None,
|
||
|
|
bint is_datetimelike=False,
|
||
|
|
ties_method="average",
|
||
|
|
bint ascending=True,
|
||
|
|
bint pct=False,
|
||
|
|
na_option="keep",
|
||
|
|
const uint8_t[:] mask=None,
|
||
|
|
):
|
||
|
|
"""
|
||
|
|
Fast NaN-friendly version of ``scipy.stats.rankdata``.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
values : array of numeric_object_t values to be ranked
|
||
|
|
labels : np.ndarray[np.intp] or None
|
||
|
|
Array containing unique label for each group, with its ordering
|
||
|
|
matching up to the corresponding record in `values`. If not called
|
||
|
|
from a groupby operation, will be None.
|
||
|
|
is_datetimelike : bool, default False
|
||
|
|
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
|
||
|
|
mask : np.ndarray[bool], optional, default None
|
||
|
|
Specify locations to be treated as NA, for e.g. Categorical.
|
||
|
|
"""
|
||
|
|
cdef:
|
||
|
|
TiebreakEnumType tiebreak
|
||
|
|
Py_ssize_t N
|
||
|
|
int64_t[::1] grp_sizes
|
||
|
|
intp_t[:] lexsort_indexer
|
||
|
|
float64_t[::1] out
|
||
|
|
ndarray[numeric_object_t, ndim=1] masked_vals
|
||
|
|
numeric_object_t[:] masked_vals_memview
|
||
|
|
bint keep_na, nans_rank_highest, check_labels, check_mask
|
||
|
|
numeric_object_t nan_fill_val
|
||
|
|
|
||
|
|
tiebreak = tiebreakers[ties_method]
|
||
|
|
if tiebreak == TIEBREAK_FIRST:
|
||
|
|
if not ascending:
|
||
|
|
tiebreak = TIEBREAK_FIRST_DESCENDING
|
||
|
|
|
||
|
|
keep_na = na_option == "keep"
|
||
|
|
|
||
|
|
N = len(values)
|
||
|
|
if labels is not None:
|
||
|
|
# TODO(cython3): cast won't be necessary (#2992)
|
||
|
|
assert <Py_ssize_t>len(labels) == N
|
||
|
|
out = np.empty(N)
|
||
|
|
grp_sizes = np.ones(N, dtype=np.int64)
|
||
|
|
|
||
|
|
# If we don't care about labels, can short-circuit later label
|
||
|
|
# comparisons
|
||
|
|
check_labels = labels is not None
|
||
|
|
|
||
|
|
# For cases where a mask is not possible, we can avoid mask checks
|
||
|
|
check_mask = (
|
||
|
|
numeric_object_t is float32_t
|
||
|
|
or numeric_object_t is float64_t
|
||
|
|
or numeric_object_t is object
|
||
|
|
or (numeric_object_t is int64_t and is_datetimelike)
|
||
|
|
)
|
||
|
|
check_mask = check_mask or mask is not None
|
||
|
|
|
||
|
|
# Copy values into new array in order to fill missing data
|
||
|
|
# with mask, without obfuscating location of missing data
|
||
|
|
# in values array
|
||
|
|
if numeric_object_t is object and values.dtype != np.object_:
|
||
|
|
masked_vals = values.astype("O")
|
||
|
|
else:
|
||
|
|
masked_vals = values.copy()
|
||
|
|
|
||
|
|
if mask is not None:
|
||
|
|
pass
|
||
|
|
elif numeric_object_t is object:
|
||
|
|
mask = missing.isnaobj(masked_vals)
|
||
|
|
elif numeric_object_t is int64_t and is_datetimelike:
|
||
|
|
mask = (masked_vals == NPY_NAT).astype(np.uint8)
|
||
|
|
elif numeric_object_t is float64_t or numeric_object_t is float32_t:
|
||
|
|
mask = np.isnan(masked_vals).astype(np.uint8)
|
||
|
|
else:
|
||
|
|
mask = np.zeros(shape=len(masked_vals), dtype=np.uint8)
|
||
|
|
|
||
|
|
# If `na_option == 'top'`, we want to assign the lowest rank
|
||
|
|
# to NaN regardless of ascending/descending. So if ascending,
|
||
|
|
# fill with lowest value of type to end up with lowest rank.
|
||
|
|
# If descending, fill with highest value since descending
|
||
|
|
# will flip the ordering to still end up with lowest rank.
|
||
|
|
# Symmetric logic applies to `na_option == 'bottom'`
|
||
|
|
nans_rank_highest = ascending ^ (na_option == "top")
|
||
|
|
nan_fill_val = get_rank_nan_fill_val(nans_rank_highest, <numeric_object_t>0)
|
||
|
|
if nans_rank_highest:
|
||
|
|
order = [masked_vals, mask]
|
||
|
|
else:
|
||
|
|
order = [masked_vals, ~(np.asarray(mask))]
|
||
|
|
|
||
|
|
if check_labels:
|
||
|
|
order.append(labels)
|
||
|
|
|
||
|
|
np.putmask(masked_vals, mask, nan_fill_val)
|
||
|
|
# putmask doesn't accept a memoryview, so we assign as a separate step
|
||
|
|
masked_vals_memview = masked_vals
|
||
|
|
|
||
|
|
# lexsort using labels, then mask, then actual values
|
||
|
|
# each label corresponds to a different group value,
|
||
|
|
# the mask helps you differentiate missing values before
|
||
|
|
# performing sort on the actual values
|
||
|
|
lexsort_indexer = np.lexsort(order).astype(np.intp, copy=False)
|
||
|
|
|
||
|
|
if not ascending:
|
||
|
|
lexsort_indexer = lexsort_indexer[::-1]
|
||
|
|
|
||
|
|
with nogil:
|
||
|
|
rank_sorted_1d(
|
||
|
|
out,
|
||
|
|
grp_sizes,
|
||
|
|
lexsort_indexer,
|
||
|
|
masked_vals_memview,
|
||
|
|
mask,
|
||
|
|
check_mask=check_mask,
|
||
|
|
N=N,
|
||
|
|
tiebreak=tiebreak,
|
||
|
|
keep_na=keep_na,
|
||
|
|
pct=pct,
|
||
|
|
labels=labels,
|
||
|
|
)
|
||
|
|
|
||
|
|
return np.asarray(out)
|
||
|
|
|
||
|
|
|
||
|
|
@cython.wraparound(False)
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
cdef void rank_sorted_1d(
|
||
|
|
float64_t[::1] out,
|
||
|
|
int64_t[::1] grp_sizes,
|
||
|
|
const intp_t[:] sort_indexer,
|
||
|
|
# TODO(cython3): make const (https://github.com/cython/cython/issues/3222)
|
||
|
|
numeric_object_t[:] masked_vals,
|
||
|
|
const uint8_t[:] mask,
|
||
|
|
bint check_mask,
|
||
|
|
Py_ssize_t N,
|
||
|
|
TiebreakEnumType tiebreak=TIEBREAK_AVERAGE,
|
||
|
|
bint keep_na=True,
|
||
|
|
bint pct=False,
|
||
|
|
# https://github.com/cython/cython/issues/1630, only trailing arguments can
|
||
|
|
# currently be omitted for cdef functions, which is why we keep this at the end
|
||
|
|
const intp_t[:] labels=None,
|
||
|
|
) nogil:
|
||
|
|
"""
|
||
|
|
See rank_1d.__doc__. Handles only actual ranking, so sorting and masking should
|
||
|
|
be handled in the caller. Note that `out` and `grp_sizes` are modified inplace.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
out : float64_t[::1]
|
||
|
|
Array to store computed ranks
|
||
|
|
grp_sizes : int64_t[::1]
|
||
|
|
Array to store group counts, only used if pct=True. Should only be None
|
||
|
|
if labels is None.
|
||
|
|
sort_indexer : intp_t[:]
|
||
|
|
Array of indices which sorts masked_vals
|
||
|
|
masked_vals : numeric_object_t[:]
|
||
|
|
The values input to rank_1d, with missing values replaced by fill values
|
||
|
|
mask : uint8_t[:]
|
||
|
|
Array where entries are True if the value is missing, False otherwise.
|
||
|
|
check_mask : bool
|
||
|
|
If False, assumes the mask is all False to skip mask indexing
|
||
|
|
N : Py_ssize_t
|
||
|
|
The number of elements to rank. Note: it is not always true that
|
||
|
|
N == len(out) or N == len(masked_vals) (see `nancorr_spearman` usage for why)
|
||
|
|
tiebreak : TiebreakEnumType, default TIEBREAK_AVERAGE
|
||
|
|
See rank_1d.__doc__ for the different modes
|
||
|
|
keep_na : bool, default True
|
||
|
|
Whether or not to keep nulls
|
||
|
|
pct : bool, default False
|
||
|
|
Compute percentage rank of data within each group
|
||
|
|
labels : See rank_1d.__doc__, default None. None implies all labels are the same.
|
||
|
|
"""
|
||
|
|
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, j, dups=0, sum_ranks=0,
|
||
|
|
Py_ssize_t grp_start=0, grp_vals_seen=1, grp_na_count=0
|
||
|
|
bint at_end, next_val_diff, group_changed, check_labels
|
||
|
|
int64_t grp_size
|
||
|
|
|
||
|
|
check_labels = labels is not None
|
||
|
|
|
||
|
|
# Loop over the length of the value array
|
||
|
|
# each incremental i value can be looked up in the lexsort_indexer
|
||
|
|
# array that we sorted previously, which gives us the location of
|
||
|
|
# that sorted value for retrieval back from the original
|
||
|
|
# values / masked_vals arrays
|
||
|
|
# TODO(cython3): de-duplicate once cython supports conditional nogil
|
||
|
|
if numeric_object_t is object:
|
||
|
|
with gil:
|
||
|
|
for i in range(N):
|
||
|
|
at_end = i == N - 1
|
||
|
|
|
||
|
|
# dups and sum_ranks will be incremented each loop where
|
||
|
|
# the value / group remains the same, and should be reset
|
||
|
|
# when either of those change. Used to calculate tiebreakers
|
||
|
|
dups += 1
|
||
|
|
sum_ranks += i - grp_start + 1
|
||
|
|
|
||
|
|
next_val_diff = at_end or are_diff(masked_vals[sort_indexer[i]],
|
||
|
|
masked_vals[sort_indexer[i+1]])
|
||
|
|
|
||
|
|
# We'll need this check later anyway to determine group size, so just
|
||
|
|
# compute it here since shortcircuiting won't help
|
||
|
|
group_changed = at_end or (check_labels and
|
||
|
|
(labels[sort_indexer[i]]
|
||
|
|
!= labels[sort_indexer[i+1]]))
|
||
|
|
|
||
|
|
# Update out only when there is a transition of values or labels.
|
||
|
|
# When a new value or group is encountered, go back #dups steps(
|
||
|
|
# the number of occurrence of current value) and assign the ranks
|
||
|
|
# based on the starting index of the current group (grp_start)
|
||
|
|
# and the current index
|
||
|
|
if (next_val_diff or group_changed or (check_mask and
|
||
|
|
(mask[sort_indexer[i]]
|
||
|
|
^ mask[sort_indexer[i+1]]))):
|
||
|
|
|
||
|
|
# If keep_na, check for missing values and assign back
|
||
|
|
# to the result where appropriate
|
||
|
|
if keep_na and check_mask and mask[sort_indexer[i]]:
|
||
|
|
grp_na_count = dups
|
||
|
|
for j in range(i - dups + 1, i + 1):
|
||
|
|
out[sort_indexer[j]] = NaN
|
||
|
|
elif tiebreak == TIEBREAK_AVERAGE:
|
||
|
|
for j in range(i - dups + 1, i + 1):
|
||
|
|
out[sort_indexer[j]] = sum_ranks / <float64_t>dups
|
||
|
|
elif tiebreak == TIEBREAK_MIN:
|
||
|
|
for j in range(i - dups + 1, i + 1):
|
||
|
|
out[sort_indexer[j]] = i - grp_start - dups + 2
|
||
|
|
elif tiebreak == TIEBREAK_MAX:
|
||
|
|
for j in range(i - dups + 1, i + 1):
|
||
|
|
out[sort_indexer[j]] = i - grp_start + 1
|
||
|
|
|
||
|
|
# With n as the previous rank in the group and m as the number
|
||
|
|
# of duplicates in this stretch, if TIEBREAK_FIRST and ascending,
|
||
|
|
# then rankings should be n + 1, n + 2 ... n + m
|
||
|
|
elif tiebreak == TIEBREAK_FIRST:
|
||
|
|
for j in range(i - dups + 1, i + 1):
|
||
|
|
out[sort_indexer[j]] = j + 1 - grp_start
|
||
|
|
|
||
|
|
# If TIEBREAK_FIRST and descending, the ranking should be
|
||
|
|
# n + m, n + (m - 1) ... n + 1. This is equivalent to
|
||
|
|
# (i - dups + 1) + (i - j + 1) - grp_start
|
||
|
|
elif tiebreak == TIEBREAK_FIRST_DESCENDING:
|
||
|
|
for j in range(i - dups + 1, i + 1):
|
||
|
|
out[sort_indexer[j]] = 2 * i - j - dups + 2 - grp_start
|
||
|
|
elif tiebreak == TIEBREAK_DENSE:
|
||
|
|
for j in range(i - dups + 1, i + 1):
|
||
|
|
out[sort_indexer[j]] = grp_vals_seen
|
||
|
|
|
||
|
|
# Look forward to the next value (using the sorting in
|
||
|
|
# lexsort_indexer). If the value does not equal the current
|
||
|
|
# value then we need to reset the dups and sum_ranks, knowing
|
||
|
|
# that a new value is coming up. The conditional also needs
|
||
|
|
# to handle nan equality and the end of iteration. If group
|
||
|
|
# changes we do not record seeing a new value in the group
|
||
|
|
if not group_changed and (next_val_diff or (check_mask and
|
||
|
|
(mask[sort_indexer[i]]
|
||
|
|
^ mask[sort_indexer[i+1]]))):
|
||
|
|
dups = sum_ranks = 0
|
||
|
|
grp_vals_seen += 1
|
||
|
|
|
||
|
|
# Similar to the previous conditional, check now if we are
|
||
|
|
# moving to a new group. If so, keep track of the index where
|
||
|
|
# the new group occurs, so the tiebreaker calculations can
|
||
|
|
# decrement that from their position. Fill in the size of each
|
||
|
|
# group encountered (used by pct calculations later). Also be
|
||
|
|
# sure to reset any of the items helping to calculate dups
|
||
|
|
if group_changed:
|
||
|
|
|
||
|
|
# If not dense tiebreak, group size used to compute
|
||
|
|
# percentile will be # of non-null elements in group
|
||
|
|
if tiebreak != TIEBREAK_DENSE:
|
||
|
|
grp_size = i - grp_start + 1 - grp_na_count
|
||
|
|
|
||
|
|
# Otherwise, it will be the number of distinct values
|
||
|
|
# in the group, subtracting 1 if NaNs are present
|
||
|
|
# since that is a distinct value we shouldn't count
|
||
|
|
else:
|
||
|
|
grp_size = grp_vals_seen - (grp_na_count > 0)
|
||
|
|
|
||
|
|
for j in range(grp_start, i + 1):
|
||
|
|
grp_sizes[sort_indexer[j]] = grp_size
|
||
|
|
|
||
|
|
dups = sum_ranks = 0
|
||
|
|
grp_na_count = 0
|
||
|
|
grp_start = i + 1
|
||
|
|
grp_vals_seen = 1
|
||
|
|
else:
|
||
|
|
for i in range(N):
|
||
|
|
at_end = i == N - 1
|
||
|
|
|
||
|
|
# dups and sum_ranks will be incremented each loop where
|
||
|
|
# the value / group remains the same, and should be reset
|
||
|
|
# when either of those change. Used to calculate tiebreakers
|
||
|
|
dups += 1
|
||
|
|
sum_ranks += i - grp_start + 1
|
||
|
|
|
||
|
|
next_val_diff = at_end or (masked_vals[sort_indexer[i]]
|
||
|
|
!= masked_vals[sort_indexer[i+1]])
|
||
|
|
|
||
|
|
# We'll need this check later anyway to determine group size, so just
|
||
|
|
# compute it here since shortcircuiting won't help
|
||
|
|
group_changed = at_end or (check_labels and
|
||
|
|
(labels[sort_indexer[i]]
|
||
|
|
!= labels[sort_indexer[i+1]]))
|
||
|
|
|
||
|
|
# Update out only when there is a transition of values or labels.
|
||
|
|
# When a new value or group is encountered, go back #dups steps(
|
||
|
|
# the number of occurrence of current value) and assign the ranks
|
||
|
|
# based on the starting index of the current group (grp_start)
|
||
|
|
# and the current index
|
||
|
|
if (next_val_diff or group_changed
|
||
|
|
or (check_mask and
|
||
|
|
(mask[sort_indexer[i]] ^ mask[sort_indexer[i+1]]))):
|
||
|
|
|
||
|
|
# If keep_na, check for missing values and assign back
|
||
|
|
# to the result where appropriate
|
||
|
|
if keep_na and check_mask and mask[sort_indexer[i]]:
|
||
|
|
grp_na_count = dups
|
||
|
|
for j in range(i - dups + 1, i + 1):
|
||
|
|
out[sort_indexer[j]] = NaN
|
||
|
|
elif tiebreak == TIEBREAK_AVERAGE:
|
||
|
|
for j in range(i - dups + 1, i + 1):
|
||
|
|
out[sort_indexer[j]] = sum_ranks / <float64_t>dups
|
||
|
|
elif tiebreak == TIEBREAK_MIN:
|
||
|
|
for j in range(i - dups + 1, i + 1):
|
||
|
|
out[sort_indexer[j]] = i - grp_start - dups + 2
|
||
|
|
elif tiebreak == TIEBREAK_MAX:
|
||
|
|
for j in range(i - dups + 1, i + 1):
|
||
|
|
out[sort_indexer[j]] = i - grp_start + 1
|
||
|
|
|
||
|
|
# With n as the previous rank in the group and m as the number
|
||
|
|
# of duplicates in this stretch, if TIEBREAK_FIRST and ascending,
|
||
|
|
# then rankings should be n + 1, n + 2 ... n + m
|
||
|
|
elif tiebreak == TIEBREAK_FIRST:
|
||
|
|
for j in range(i - dups + 1, i + 1):
|
||
|
|
out[sort_indexer[j]] = j + 1 - grp_start
|
||
|
|
|
||
|
|
# If TIEBREAK_FIRST and descending, the ranking should be
|
||
|
|
# n + m, n + (m - 1) ... n + 1. This is equivalent to
|
||
|
|
# (i - dups + 1) + (i - j + 1) - grp_start
|
||
|
|
elif tiebreak == TIEBREAK_FIRST_DESCENDING:
|
||
|
|
for j in range(i - dups + 1, i + 1):
|
||
|
|
out[sort_indexer[j]] = 2 * i - j - dups + 2 - grp_start
|
||
|
|
elif tiebreak == TIEBREAK_DENSE:
|
||
|
|
for j in range(i - dups + 1, i + 1):
|
||
|
|
out[sort_indexer[j]] = grp_vals_seen
|
||
|
|
|
||
|
|
# Look forward to the next value (using the sorting in
|
||
|
|
# lexsort_indexer). If the value does not equal the current
|
||
|
|
# value then we need to reset the dups and sum_ranks, knowing
|
||
|
|
# that a new value is coming up. The conditional also needs
|
||
|
|
# to handle nan equality and the end of iteration. If group
|
||
|
|
# changes we do not record seeing a new value in the group
|
||
|
|
if not group_changed and (next_val_diff
|
||
|
|
or (check_mask and
|
||
|
|
(mask[sort_indexer[i]]
|
||
|
|
^ mask[sort_indexer[i+1]]))):
|
||
|
|
dups = sum_ranks = 0
|
||
|
|
grp_vals_seen += 1
|
||
|
|
|
||
|
|
# Similar to the previous conditional, check now if we are
|
||
|
|
# moving to a new group. If so, keep track of the index where
|
||
|
|
# the new group occurs, so the tiebreaker calculations can
|
||
|
|
# decrement that from their position. Fill in the size of each
|
||
|
|
# group encountered (used by pct calculations later). Also be
|
||
|
|
# sure to reset any of the items helping to calculate dups
|
||
|
|
if group_changed:
|
||
|
|
|
||
|
|
# If not dense tiebreak, group size used to compute
|
||
|
|
# percentile will be # of non-null elements in group
|
||
|
|
if tiebreak != TIEBREAK_DENSE:
|
||
|
|
grp_size = i - grp_start + 1 - grp_na_count
|
||
|
|
|
||
|
|
# Otherwise, it will be the number of distinct values
|
||
|
|
# in the group, subtracting 1 if NaNs are present
|
||
|
|
# since that is a distinct value we shouldn't count
|
||
|
|
else:
|
||
|
|
grp_size = grp_vals_seen - (grp_na_count > 0)
|
||
|
|
|
||
|
|
for j in range(grp_start, i + 1):
|
||
|
|
grp_sizes[sort_indexer[j]] = grp_size
|
||
|
|
|
||
|
|
dups = sum_ranks = 0
|
||
|
|
grp_na_count = 0
|
||
|
|
grp_start = i + 1
|
||
|
|
grp_vals_seen = 1
|
||
|
|
|
||
|
|
if pct:
|
||
|
|
for i in range(N):
|
||
|
|
if grp_sizes[i] != 0:
|
||
|
|
out[i] = out[i] / grp_sizes[i]
|
||
|
|
|
||
|
|
|
||
|
|
def rank_2d(
|
||
|
|
ndarray[numeric_object_t, ndim=2] in_arr,
|
||
|
|
int axis=0,
|
||
|
|
bint is_datetimelike=False,
|
||
|
|
ties_method="average",
|
||
|
|
bint ascending=True,
|
||
|
|
na_option="keep",
|
||
|
|
bint pct=False,
|
||
|
|
):
|
||
|
|
"""
|
||
|
|
Fast NaN-friendly version of ``scipy.stats.rankdata``.
|
||
|
|
"""
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t k, n, col
|
||
|
|
float64_t[::1, :] out # Column-major so columns are contiguous
|
||
|
|
int64_t[::1] grp_sizes
|
||
|
|
ndarray[numeric_object_t, ndim=2] values
|
||
|
|
numeric_object_t[:, :] masked_vals
|
||
|
|
intp_t[:, :] sort_indexer
|
||
|
|
uint8_t[:, :] mask
|
||
|
|
TiebreakEnumType tiebreak
|
||
|
|
bint check_mask, keep_na, nans_rank_highest
|
||
|
|
numeric_object_t nan_fill_val
|
||
|
|
|
||
|
|
tiebreak = tiebreakers[ties_method]
|
||
|
|
if tiebreak == TIEBREAK_FIRST:
|
||
|
|
if not ascending:
|
||
|
|
tiebreak = TIEBREAK_FIRST_DESCENDING
|
||
|
|
|
||
|
|
keep_na = na_option == "keep"
|
||
|
|
|
||
|
|
# For cases where a mask is not possible, we can avoid mask checks
|
||
|
|
check_mask = (
|
||
|
|
numeric_object_t is float32_t
|
||
|
|
or numeric_object_t is float64_t
|
||
|
|
or numeric_object_t is object
|
||
|
|
or (numeric_object_t is int64_t and is_datetimelike)
|
||
|
|
)
|
||
|
|
|
||
|
|
if axis == 1:
|
||
|
|
values = np.asarray(in_arr).T.copy()
|
||
|
|
else:
|
||
|
|
values = np.asarray(in_arr).copy()
|
||
|
|
|
||
|
|
if numeric_object_t is object:
|
||
|
|
if values.dtype != np.object_:
|
||
|
|
values = values.astype("O")
|
||
|
|
|
||
|
|
nans_rank_highest = ascending ^ (na_option == "top")
|
||
|
|
if check_mask:
|
||
|
|
nan_fill_val = get_rank_nan_fill_val(nans_rank_highest, <numeric_object_t>0)
|
||
|
|
|
||
|
|
if numeric_object_t is object:
|
||
|
|
mask = missing.isnaobj(values).view(np.uint8)
|
||
|
|
elif numeric_object_t is float64_t or numeric_object_t is float32_t:
|
||
|
|
mask = np.isnan(values).view(np.uint8)
|
||
|
|
else:
|
||
|
|
# i.e. int64 and datetimelike
|
||
|
|
mask = (values == NPY_NAT).view(np.uint8)
|
||
|
|
np.putmask(values, mask, nan_fill_val)
|
||
|
|
else:
|
||
|
|
mask = np.zeros_like(values, dtype=np.uint8)
|
||
|
|
|
||
|
|
if nans_rank_highest:
|
||
|
|
order = (values, mask)
|
||
|
|
else:
|
||
|
|
order = (values, ~np.asarray(mask))
|
||
|
|
|
||
|
|
n, k = (<object>values).shape
|
||
|
|
out = np.empty((n, k), dtype="f8", order="F")
|
||
|
|
grp_sizes = np.ones(n, dtype=np.int64)
|
||
|
|
|
||
|
|
# lexsort is slower, so only use if we need to worry about the mask
|
||
|
|
if check_mask:
|
||
|
|
sort_indexer = np.lexsort(order, axis=0).astype(np.intp, copy=False)
|
||
|
|
else:
|
||
|
|
kind = "stable" if ties_method == "first" else None
|
||
|
|
sort_indexer = values.argsort(axis=0, kind=kind).astype(np.intp, copy=False)
|
||
|
|
|
||
|
|
if not ascending:
|
||
|
|
sort_indexer = sort_indexer[::-1, :]
|
||
|
|
|
||
|
|
# putmask doesn't accept a memoryview, so we assign in a separate step
|
||
|
|
masked_vals = values
|
||
|
|
with nogil:
|
||
|
|
for col in range(k):
|
||
|
|
rank_sorted_1d(
|
||
|
|
out[:, col],
|
||
|
|
grp_sizes,
|
||
|
|
sort_indexer[:, col],
|
||
|
|
masked_vals[:, col],
|
||
|
|
mask[:, col],
|
||
|
|
check_mask=check_mask,
|
||
|
|
N=n,
|
||
|
|
tiebreak=tiebreak,
|
||
|
|
keep_na=keep_na,
|
||
|
|
pct=pct,
|
||
|
|
)
|
||
|
|
|
||
|
|
if axis == 1:
|
||
|
|
return np.asarray(out.T)
|
||
|
|
else:
|
||
|
|
return np.asarray(out)
|
||
|
|
|
||
|
|
|
||
|
|
ctypedef fused diff_t:
|
||
|
|
float64_t
|
||
|
|
float32_t
|
||
|
|
int8_t
|
||
|
|
int16_t
|
||
|
|
int32_t
|
||
|
|
int64_t
|
||
|
|
|
||
|
|
ctypedef fused out_t:
|
||
|
|
float32_t
|
||
|
|
float64_t
|
||
|
|
int64_t
|
||
|
|
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
@cython.wraparound(False)
|
||
|
|
def diff_2d(
|
||
|
|
ndarray[diff_t, ndim=2] arr, # TODO(cython3) update to "const diff_t[:, :] arr"
|
||
|
|
ndarray[out_t, ndim=2] out,
|
||
|
|
Py_ssize_t periods,
|
||
|
|
int axis,
|
||
|
|
bint datetimelike=False,
|
||
|
|
):
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, j, sx, sy, start, stop
|
||
|
|
bint f_contig = arr.flags.f_contiguous
|
||
|
|
# bint f_contig = arr.is_f_contig() # TODO(cython3)
|
||
|
|
diff_t left, right
|
||
|
|
|
||
|
|
# Disable for unsupported dtype combinations,
|
||
|
|
# see https://github.com/cython/cython/issues/2646
|
||
|
|
if (out_t is float32_t
|
||
|
|
and not (diff_t is float32_t or diff_t is int8_t or diff_t is int16_t)):
|
||
|
|
raise NotImplementedError # pragma: no cover
|
||
|
|
elif (out_t is float64_t
|
||
|
|
and (diff_t is float32_t or diff_t is int8_t or diff_t is int16_t)):
|
||
|
|
raise NotImplementedError # pragma: no cover
|
||
|
|
elif out_t is int64_t and diff_t is not int64_t:
|
||
|
|
# We only have out_t of int64_t if we have datetimelike
|
||
|
|
raise NotImplementedError # pragma: no cover
|
||
|
|
else:
|
||
|
|
# We put this inside an indented else block to avoid cython build
|
||
|
|
# warnings about unreachable code
|
||
|
|
sx, sy = (<object>arr).shape
|
||
|
|
with nogil:
|
||
|
|
if f_contig:
|
||
|
|
if axis == 0:
|
||
|
|
if periods >= 0:
|
||
|
|
start, stop = periods, sx
|
||
|
|
else:
|
||
|
|
start, stop = 0, sx + periods
|
||
|
|
for j in range(sy):
|
||
|
|
for i in range(start, stop):
|
||
|
|
left = arr[i, j]
|
||
|
|
right = arr[i - periods, j]
|
||
|
|
if out_t is int64_t and datetimelike:
|
||
|
|
if left == NPY_NAT or right == NPY_NAT:
|
||
|
|
out[i, j] = NPY_NAT
|
||
|
|
else:
|
||
|
|
out[i, j] = left - right
|
||
|
|
else:
|
||
|
|
out[i, j] = left - right
|
||
|
|
else:
|
||
|
|
if periods >= 0:
|
||
|
|
start, stop = periods, sy
|
||
|
|
else:
|
||
|
|
start, stop = 0, sy + periods
|
||
|
|
for j in range(start, stop):
|
||
|
|
for i in range(sx):
|
||
|
|
left = arr[i, j]
|
||
|
|
right = arr[i, j - periods]
|
||
|
|
if out_t is int64_t and datetimelike:
|
||
|
|
if left == NPY_NAT or right == NPY_NAT:
|
||
|
|
out[i, j] = NPY_NAT
|
||
|
|
else:
|
||
|
|
out[i, j] = left - right
|
||
|
|
else:
|
||
|
|
out[i, j] = left - right
|
||
|
|
else:
|
||
|
|
if axis == 0:
|
||
|
|
if periods >= 0:
|
||
|
|
start, stop = periods, sx
|
||
|
|
else:
|
||
|
|
start, stop = 0, sx + periods
|
||
|
|
for i in range(start, stop):
|
||
|
|
for j in range(sy):
|
||
|
|
left = arr[i, j]
|
||
|
|
right = arr[i - periods, j]
|
||
|
|
if out_t is int64_t and datetimelike:
|
||
|
|
if left == NPY_NAT or right == NPY_NAT:
|
||
|
|
out[i, j] = NPY_NAT
|
||
|
|
else:
|
||
|
|
out[i, j] = left - right
|
||
|
|
else:
|
||
|
|
out[i, j] = left - right
|
||
|
|
else:
|
||
|
|
if periods >= 0:
|
||
|
|
start, stop = periods, sy
|
||
|
|
else:
|
||
|
|
start, stop = 0, sy + periods
|
||
|
|
for i in range(sx):
|
||
|
|
for j in range(start, stop):
|
||
|
|
left = arr[i, j]
|
||
|
|
right = arr[i, j - periods]
|
||
|
|
if out_t is int64_t and datetimelike:
|
||
|
|
if left == NPY_NAT or right == NPY_NAT:
|
||
|
|
out[i, j] = NPY_NAT
|
||
|
|
else:
|
||
|
|
out[i, j] = left - right
|
||
|
|
else:
|
||
|
|
out[i, j] = left - right
|
||
|
|
|
||
|
|
|
||
|
|
# generated from template
|
||
|
|
include "algos_common_helper.pxi"
|
||
|
|
include "algos_take_helper.pxi"
|