def test_me_case_1():
a = ny.array([[10, float('NaN'), 4], [float('NaN'), 2, 1]])
na = ny.array(a)
ans = ny.nanstd(na, axis=0)
out = ny.zeros_like(ans)
dst = ny.nanstd(na, axis=0, out=out)
assert_array_almost_equal(dst.get(), out.get())
def ca5(arg):
d = arg[0]
t = arg[1]
a = arg[2]
k = arg[4]
ss = 1
for x in d:
ss *= x
if t == 'int32' or t == 'int64' or t == 'uint32' or t == 'uint64':
seed = [random.randint(1, ss * ss) for i in range(ss)]
elif t == 'float64' or t == 'float32':
seed = [random.random() for i in range(ss)]
else:
pass
na = np.array(seed)
ans = np.nanstd(na, axis=a, keepdims=k)
np.save(fil, ans)
ok = np.load(fil)
dst = ny.nanstd(na, axis=a, keepdims=k)
return ok, dst
def test_me_case_3():
ny_a = ny.array([[10, float('NaN'), 4], [3, 2, 1]])
np_a = np.array([[10, float('NaN'), 4], [3, 2, 1]])
ny_ans = ny.nanstd(ny_a, ddof=1)
np_ans = np.nanstd(np_a, ddof=1)
assert_array_almost_equal(ny_ans.get(), np_ans)
def ca1(arg):
d = arg[0]
na = np.array(d)
src = np.nanstd(na)
np.save(fil, src)
ok = np.load(fil)
dst = ny.nanstd(na)
return ok, dst
def ca2(arg):
d = arg[0]
a = arg[2]
na = np.array(d)
src = np.nanstd(na, axis=a)
np.save(fil, src)
ok = np.load(fil)
dst = ny.nanstd(na, axis=a)
return ok, dst
def ca4(arg):
d = arg[0]
t = arg[1]
a = arg[2]
ss = 1
for x in d:
ss *= x
if t == 'int32' or t == 'int64' or t == 'uint32' or t == 'uint64':
seed = [random.randint(1, ss * ss) for i in range(ss)]
elif t == 'float64' or t == 'float32':
seed = [random.random() for i in range(ss)]
else:
pass
na = ny.array(seed)
ans = ny.nanstd(na, axis=a)
out = ny.zeros_like(ans)
dst = ny.nanstd(na, axis=a, out=out)
return dst, out