def sqrtxy(x, y, p):
if is_np(x) and is_np(y):
new_dim = min_dim(x, y)
return np.sqrt(
np.square(np.resize(x, new_dim)) +
np.square(np.resize(y, new_dim))) / np.sqrt(2.0)
return np.sqrt(np.square(x) + np.square(y)) / np.sqrt(2.0)
def inv(x, y, p):
if is_np(x):
out = np.zeros(x.shape)
np.divide(1.0, x, out=out, where=(x != 0.0))
return out
else:
return x if x == 0 else 1.0 / x
def f_range(x, y, p):
if is_np(x):
return np.max(x) - np.min(x) - 1
else:
return x
def kurtosis(x, y, p):
if is_np(x):
return scipy.stats.kurtosis(x)
else:
return x
def skew(x, y, p):
if is_np(x):
return scipy.stats.skew(x)
else:
return x
def stddev(x, y, p):
if is_np(x):
return np.std(x)
else:
return x
def reverse(x, y, p):
if not is_np(x):
return x
else:
return np.copy(x[::-1])
def ypow(x, y, p):
if is_np(x) and is_np(y):
new_dim = min_dim(x, y)
return (np.abs(np.resize(x, new_dim))**np.abs(np.resize(y, new_dim)))
return np.abs(x)**np.abs(y)
def mult(x, y, p):
if is_np(x) and is_np(y):
new_dim = min_dim(x, y)
return np.resize(x, new_dim) * np.resize(y, new_dim)
return x * y
def aminus(x, y, p):
if is_np(x) and is_np(y):
new_dim = min_dim(x, y)
return np.abs(np.resize(x, new_dim) - np.resize(y, new_dim)) / 2.0
return np.abs(x - y) / 2.0
def add(x, y, p):
if is_np(x) and is_np(y):
new_dim = min_dim(x, y)
return (np.resize(x, new_dim) + np.resize(y, new_dim)) / 2.0
return (x + y) / 2.0
def gt(x, y, p):
if is_np(x) and is_np(y):
new_dim = min_dim(x, y)
return np.resize(x, new_dim) > np.resize(y, new_dim)
return x > y
