def sliding_var(
values: np.ndarray,
start: np.ndarray,
end: np.ndarray,
min_periods: int,
ddof: int = 1,
) -> np.ndarray:
N = len(start)
nobs = 0
mean_x = 0.0
ssqdm_x = 0.0
compensation_add = 0.0
compensation_remove = 0.0
min_periods = max(min_periods, 1)
is_monotonic_increasing_bounds = is_monotonic_increasing(
start
) and is_monotonic_increasing(end)
output = np.empty(N, dtype=np.float64)
for i in range(N):
s = start[i]
e = end[i]
if i == 0 or not is_monotonic_increasing_bounds:
for j in range(s, e):
val = values[j]
nobs, mean_x, ssqdm_x, compensation_add = add_var(
val, nobs, mean_x, ssqdm_x, compensation_add
)
else:
for j in range(start[i - 1], s):
val = values[j]
nobs, mean_x, ssqdm_x, compensation_remove = remove_var(
val, nobs, mean_x, ssqdm_x, compensation_remove
)
for j in range(end[i - 1], e):
val = values[j]
nobs, mean_x, ssqdm_x, compensation_add = add_var(
val, nobs, mean_x, ssqdm_x, compensation_add
)
if nobs >= min_periods and nobs > ddof:
if nobs == 1:
result = 0.0
else:
result = ssqdm_x / (nobs - ddof)
else:
result = np.nan
output[i] = result
if not is_monotonic_increasing_bounds:
nobs = 0
mean_x = 0.0
ssqdm_x = 0.0
compensation_remove = 0.0
return output
def sliding_mean(
values: np.ndarray,
start: np.ndarray,
end: np.ndarray,
min_periods: int,
) -> np.ndarray:
N = len(start)
nobs = 0
sum_x = 0.0
neg_ct = 0
compensation_add = 0.0
compensation_remove = 0.0
is_monotonic_increasing_bounds = is_monotonic_increasing(
start) and is_monotonic_increasing(end)
output = np.empty(N, dtype=np.float64)
for i in range(N):
s = start[i]
e = end[i]
if i == 0 or not is_monotonic_increasing_bounds:
for j in range(s, e):
val = values[j]
nobs, sum_x, neg_ct, compensation_add = add_mean(
val, nobs, sum_x, neg_ct, compensation_add)
else:
for j in range(start[i - 1], s):
val = values[j]
nobs, sum_x, neg_ct, compensation_remove = remove_mean(
val, nobs, sum_x, neg_ct, compensation_remove)
for j in range(end[i - 1], e):
val = values[j]
nobs, sum_x, neg_ct, compensation_add = add_mean(
val, nobs, sum_x, neg_ct, compensation_add)
if nobs >= min_periods and nobs > 0:
result = sum_x / nobs
if neg_ct == 0 and result < 0:
result = 0
elif neg_ct == nobs and result > 0:
result = 0
else:
result = np.nan
output[i] = result
if not is_monotonic_increasing_bounds:
nobs = 0
sum_x = 0.0
neg_ct = 0
compensation_remove = 0.0
return output
def sliding_sum(
values: np.ndarray,
start: np.ndarray,
end: np.ndarray,
min_periods: int,
) -> np.ndarray:
N = len(start)
nobs = 0
sum_x = 0.0
compensation_add = 0.0
compensation_remove = 0.0
is_monotonic_increasing_bounds = is_monotonic_increasing(
start) and is_monotonic_increasing(end)
output = np.empty(N, dtype=np.float64)
for i in range(N):
s = start[i]
e = end[i]
if i == 0 or not is_monotonic_increasing_bounds:
prev_value = values[s]
num_consecutive_same_value = 0
for j in range(s, e):
val = values[j]
(
nobs,
sum_x,
compensation_add,
num_consecutive_same_value,
prev_value,
) = add_sum(
val,
nobs,
sum_x,
compensation_add,
num_consecutive_same_value,
prev_value,
)
else:
for j in range(start[i - 1], s):
val = values[j]
nobs, sum_x, compensation_remove = remove_sum(
val, nobs, sum_x, compensation_remove)
for j in range(end[i - 1], e):
val = values[j]
(
nobs,
sum_x,
compensation_add,
num_consecutive_same_value,
prev_value,
) = add_sum(
val,
nobs,
sum_x,
compensation_add,
num_consecutive_same_value,
prev_value,
)
if nobs == 0 == min_periods:
result = 0.0
elif nobs >= min_periods:
if num_consecutive_same_value >= nobs:
result = prev_value * nobs
else:
result = sum_x
else:
result = np.nan
output[i] = result
if not is_monotonic_increasing_bounds:
nobs = 0
sum_x = 0.0
compensation_remove = 0.0
return output