You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
139 lines
3.3 KiB
139 lines
3.3 KiB
cimport cython
|
|
from cython cimport Py_ssize_t
|
|
from numpy cimport (
|
|
int64_t,
|
|
ndarray,
|
|
uint8_t,
|
|
)
|
|
|
|
import numpy as np
|
|
|
|
cimport numpy as cnp
|
|
from libc.math cimport NAN
|
|
|
|
cnp.import_array()
|
|
|
|
from pandas._libs.dtypes cimport numeric_object_t
|
|
from pandas._libs.lib cimport c_is_list_like
|
|
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
def unstack(numeric_object_t[:, :] values, const uint8_t[:] mask,
|
|
Py_ssize_t stride, Py_ssize_t length, Py_ssize_t width,
|
|
numeric_object_t[:, :] new_values, uint8_t[:, :] new_mask) -> None:
|
|
"""
|
|
Transform long values to wide new_values.
|
|
|
|
Parameters
|
|
----------
|
|
values : typed ndarray
|
|
mask : np.ndarray[bool]
|
|
stride : int
|
|
length : int
|
|
width : int
|
|
new_values : np.ndarray[bool]
|
|
result array
|
|
new_mask : np.ndarray[bool]
|
|
result mask
|
|
"""
|
|
cdef:
|
|
Py_ssize_t i, j, w, nulls, s, offset
|
|
|
|
if numeric_object_t is not object:
|
|
# evaluated at compile-time
|
|
with nogil:
|
|
for i in range(stride):
|
|
|
|
nulls = 0
|
|
for j in range(length):
|
|
|
|
for w in range(width):
|
|
|
|
offset = j * width + w
|
|
|
|
if mask[offset]:
|
|
s = i * width + w
|
|
new_values[j, s] = values[offset - nulls, i]
|
|
new_mask[j, s] = 1
|
|
else:
|
|
nulls += 1
|
|
|
|
else:
|
|
# object-dtype, identical to above but we cannot use nogil
|
|
for i in range(stride):
|
|
|
|
nulls = 0
|
|
for j in range(length):
|
|
|
|
for w in range(width):
|
|
|
|
offset = j * width + w
|
|
|
|
if mask[offset]:
|
|
s = i * width + w
|
|
new_values[j, s] = values[offset - nulls, i]
|
|
new_mask[j, s] = 1
|
|
else:
|
|
nulls += 1
|
|
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
def explode(ndarray[object] values):
|
|
"""
|
|
transform array list-likes to long form
|
|
preserve non-list entries
|
|
|
|
Parameters
|
|
----------
|
|
values : ndarray[object]
|
|
|
|
Returns
|
|
-------
|
|
ndarray[object]
|
|
result
|
|
ndarray[int64_t]
|
|
counts
|
|
"""
|
|
cdef:
|
|
Py_ssize_t i, j, count, n
|
|
object v
|
|
ndarray[object] result
|
|
ndarray[int64_t] counts
|
|
|
|
# find the resulting len
|
|
n = len(values)
|
|
counts = np.zeros(n, dtype="int64")
|
|
for i in range(n):
|
|
v = values[i]
|
|
|
|
if c_is_list_like(v, True):
|
|
if len(v):
|
|
counts[i] += len(v)
|
|
else:
|
|
# empty list-like, use a nan marker
|
|
counts[i] += 1
|
|
else:
|
|
counts[i] += 1
|
|
|
|
result = np.empty(counts.sum(), dtype="object")
|
|
count = 0
|
|
for i in range(n):
|
|
v = values[i]
|
|
|
|
if c_is_list_like(v, True):
|
|
if len(v):
|
|
v = list(v)
|
|
for j in range(len(v)):
|
|
result[count] = v[j]
|
|
count += 1
|
|
else:
|
|
# empty list-like, use a nan marker
|
|
result[count] = NAN
|
|
count += 1
|
|
else:
|
|
# replace with the existing scalar
|
|
result[count] = v
|
|
count += 1
|
|
return result, counts
|
|
|