def check_expand_and_squeeze(_np_a, axes):
_name = 'matmul'
np_expand_dims = np.__getattribute__('expand_dims')
ns_expand_dims = nps.__getattribute__('expand_dims')
np_squeeze = np.__getattribute__('squeeze')
ns_squeeze = nps.__getattribute__('squeeze')
_ns_a = nps.array(_np_a)
_np_result = np_expand_dims(_np_a, axes)
_ns_result = ns_expand_dims(_ns_a, axes)
assert np.allclose(_np_result, _ns_result.get())
check_dim(_np_result, _ns_result)
_np_result = np_squeeze(_np_a)
_ns_result = ns_squeeze(_ns_a)
assert np.allclose(_np_result, _ns_result.get())
check_dim(_np_result, _ns_result)
def test_doctest_fallback(nps_app_inst):
import nums.numpy as nps
assert nps_app_inst is not None
passed = []
failed = []
excepted = []
plot_funcs = {
"kaiser",
"bartlett",
"hanning",
"blackman",
"histogram2d",
"interp",
"sinc",
}
for func in settings.doctest_fallbacks:
nps_func = nps.__getattribute__(func)
nps_func.__doc__ = update_doc_string(np.__getattribute__(func).__doc__)
f = io.StringIO()
with redirect_stdout(f):
nps_func = nps.__getattribute__(func)
if func in plot_funcs:
# Skip plot functions and add them to the failed list
print("Failure")
else:
optionflags = doctest.NORMALIZE_WHITESPACE | doctest.FAIL_FAST
doctest.run_docstring_examples(
nps_func, locals(), optionflags=optionflags
)
if f.getvalue() == "":
passed.append(func)
else:
failed.append(func)
print("***DOCTESTS***")
print("PASSED:")
print(sorted(passed))
print("FAILED:")
print(sorted(failed))
print("EXCEPTED:")
print(sorted(excepted))
def test_nan_reductions(nps_app_inst):
from nums import numpy as nps
from nums.numpy import BlockArray
assert nps_app_inst is not None
ba: BlockArray = nps.array([[-1, 4, np.nan, 5], [3, 2, nps.nan, 6]])
block_shapes = [(1, 1), (1, 2), (1, 4), (2, 1), (2, 4)]
for block_shape in block_shapes:
ba = ba.reshape(block_shape=block_shape)
np_arr = ba.get()
op_params = ["nanmax", "nanmin", "nansum", "nanmean", "nanvar", "nanstd"]
axis_params = [None, 0, 1]
keepdims_params = [True, False]
for op, axis, keepdims in itertools.product(
op_params, axis_params, keepdims_params
):
ns_op = nps.__getattribute__(op)
np_op = np.__getattribute__(op)
np_result = np_op(np_arr, axis=axis, keepdims=keepdims)
ba_result: BlockArray = ns_op(ba, axis=axis, keepdims=keepdims)
assert ba_result.grid.grid_shape == ba_result.blocks.shape
assert ba_result.shape == np_result.shape
assert np.allclose(ba_result.get(), np_result, equal_nan=True)
def test_stats_1d(nps_app_inst):
from nums import numpy as nps
from nums.numpy import BlockArray
assert nps_app_inst is not None
ba: BlockArray = nps.array([5, -2, 4, 8, 3, 6, 1, 7])
block_shapes = [(1, ), (2, ), (4, ), (8, )]
qs = [0, 50, 100]
for block_shape in block_shapes:
ba = ba.reshape(block_shape=block_shape)
np_arr = ba.get()
op_params = ["median", "percentile", "quantile"]
axis_params = [None]
keepdims_params = [False]
for op, q, axis, keepdims in itertools.product(op_params, qs,
axis_params,
keepdims_params):
ns_op = nps.__getattribute__(op)
np_op = np.__getattribute__(op)
if op == "median":
np_result = np_op(np_arr, axis=axis, keepdims=keepdims)
ba_result: BlockArray = ns_op(ba, axis=axis, keepdims=keepdims)
elif op == "quantile":
q = q / 100
np_result = np_op(np_arr, q, axis=axis, keepdims=keepdims)
ba_result: BlockArray = ns_op(ba,
q,
axis=axis,
keepdims=keepdims)
assert ba_result.grid.grid_shape == ba_result.blocks.shape
assert ba_result.size == np_result.size
assert np.allclose(ba_result.get(), np_result)
def test_argops(nps_app_inst):
from nums import numpy as nps
from nums.numpy import BlockArray
assert nps_app_inst is not None
bas = [
nps.array([5, -2, 4, 8]),
nps.array([1, 2, 3, 4]),
nps.array([3, 2, 1, 0]),
nps.array([-1, -2, -3, -0]),
]
block_shapes = [(1, ), (2, ), (3, ), (4, )]
for ba in bas:
for block_shape in block_shapes:
ba = ba.reshape(block_shape=block_shape)
np_arr = ba.get()
op_params = ["argmin", "argmax"]
axis_params = [None, 0]
for op, axis in itertools.product(op_params, axis_params):
ns_op = nps.__getattribute__(op)
np_op = np.__getattribute__(op)
np_result = np_op(np_arr, axis=axis)
ba_result: BlockArray = ns_op(ba, axis=axis)
assert ba_result.grid.grid_shape == ba_result.blocks.shape
assert ba_result.shape == np_result.shape
assert np.allclose(ba_result.get(), np_result)
def test_ufunc(nps_app_inst):
import numpy as np
from nums.numpy import BlockArray
from nums.numpy import numpy_utils
from nums import numpy as nps
assert nps_app_inst is not None
uops, bops = numpy_utils.ufunc_op_signatures()
for name, _ in sorted(uops):
if name in ("arccosh", "arcsinh"):
np_val = np.array([np.e])
elif name == "invert" or name.startswith("bitwise") or name.startswith(
"logical"):
np_val = np.array([True, False], dtype=np.bool_)
else:
np_val = np.array([.1, .2, .3])
ns_val = nps.array(np_val)
ns_ufunc = nps.__getattribute__(name)
np_ufunc = np.__getattribute__(name)
np_result = np_ufunc(np_val)
ns_result: BlockArray = ns_ufunc(ns_val)
assert np.allclose(np_result, ns_result.get())
def check_bop(_name, _np_a, _np_b):
np_ufunc = np.__getattribute__(_name)
ns_ufunc = nps.__getattribute__(_name)
if _name in ("ldexp", ) and str(
_np_b.dtype) not in ("int", "int32", "int64"):
return
_ns_a = nps.array(_np_a)
_ns_b = nps.array(_np_b)
_np_result = np_ufunc(_np_a, _np_b)
_ns_result = ns_ufunc(_ns_a, _ns_b)
assert np.allclose(_np_result, _ns_result.get())
for name, _ in bops:
if name.startswith("bitwise") or name.startswith("logical"):
np_a = np.array([True, False, True, False], dtype=np.bool_)
np_b = np.array([True, True, False, False], dtype=np.bool_)
check_bop(name, np_a, np_b)
elif name in ("gcd", "lcm"):
np_a = np.array([8, 3, 7], dtype=np.int)
np_b = np.array([4, 12, 13], dtype=np.int)
check_bop(name, np_a, np_b)
elif name.endswith("shift"):
np_a = np.array([7 * 10**3, 8 * 10**3, 9 * 10**3], dtype=np.int)
np_b = np.array([1, 2, 3], dtype=np.int)
check_bop(name, np_a, np_b)
else:
pairs = [
(np.array([.1, 5.0, .3]), np.array([.2, 6.0, .3])),
(np.array([.1, 5.0, .3]), np.array([4, 2, 6], dtype=np.int)),
(np.array([3, 7, 3],
dtype=np.int), np.array([4, 2, 6], dtype=np.int)),
]
for np_a, np_b in pairs:
check_bop(name, np_a, np_b)
def check_tensordot_op(_np_a, _np_b, axes):
_name = "tensordot"
np_ufunc = np.__getattribute__(_name)
ns_ufunc = nps.__getattribute__(_name)
_ns_a = nps.array(_np_a)
_ns_b = nps.array(_np_b)
_np_result = np_ufunc(_np_a, _np_b, axes=axes)
_ns_result = ns_ufunc(_ns_a, _ns_b, axes=axes)
assert np.allclose(_np_result, _ns_result.get())
def check_matmul_op(_np_a, _np_b):
_name = "matmul"
np_ufunc = np.__getattribute__(_name)
ns_ufunc = nps.__getattribute__(_name)
_ns_a = nps.array(_np_a)
_ns_b = nps.array(_np_b)
_np_result = np_ufunc(_np_a, _np_b)
_ns_result = ns_ufunc(_ns_a, _ns_b)
assert np.allclose(_np_result, _ns_result.get())
def test_basic_creation(nps_app_inst):
import nums.numpy as nps
assert nps_app_inst is not None
ops = "empty", "zeros", "ones"
shape = (2, 3, 4)
for op in ops:
ba: BlockArray = nps.__getattribute__(op)(shape=shape)
if "zeros" in op:
assert nps.allclose(nps.zeros(shape), ba)
if "ones" in op:
assert nps.allclose(nps.ones(shape), ba)
ba2: BlockArray = nps.__getattribute__(op + "_like")(ba)
assert ba.shape == ba2.shape
assert ba.dtype == ba2.dtype
assert ba.block_shape == ba2.block_shape
def check_bop(_name, _np_a, _np_b):
np_ufunc = np.__getattribute__(_name)
ns_ufunc = nps.__getattribute__(_name)
if _name in ("ldexp",) and str(_np_b.dtype) not in ("int", "int32", "int64"):
return
_ns_a = nps.array(_np_a)
_ns_b = nps.array(_np_b)
_np_result = np_ufunc(_np_a, _np_b)
_ns_result = ns_ufunc(_ns_a, _ns_b)
assert np.allclose(_np_result, _ns_result.get())
def check_value_error(_ns_a, _ns_b, _a_blockshape=None, _b_blockshape=None):
for _op in _ops:
ns_op = nps.__getattribute__(_op)
if _a_blockshape:
_ns_a = _ns_a.reshape(block_shape=_a_blockshape)
if _b_blockshape:
_ns_b = _ns_b.reshape(block_shape=_b_blockshape)
with pytest.raises(ValueError):
ns_op(_ns_a, _ns_b)
def check_swapaxes(_np_a, axis1, axis2):
ns_ins = application_manager.instance()
np_swapaxes = np.__getattribute__("swapaxes")
ns_swapaxes = nps.__getattribute__("swapaxes")
_ns_a = nps.array(_np_a)
_ns_ins_a = ns_ins.array(_np_a, block_shape=block_shape)
_np_result = np_swapaxes(_np_a, axis1, axis2)
_ns_result = ns_swapaxes(_ns_a, axis1, axis2)
_ns_ins_result = ns_swapaxes(_ns_ins_a, axis1, axis2)
assert np.allclose(_np_result, _ns_result.get())
assert np.allclose(_np_result, _ns_ins_result.get())
def test_manual(nps_app_inst):
assert nps_app_inst is not None
import nums.numpy as nps
test_set = {}
test_set.update(get_test_set_a())
test_set.update(get_test_set_b2f())
test_set.update(get_test_set_g2n())
test_set.update(get_test_set_o2z())
for func_name in test_set:
nps_func = nps.__getattribute__(func_name)
np_func = np.__getattribute__(func_name)
for params in test_set[func_name]:
assert np.allclose(nps_func(*params).get(), np_func(*params))
def check_expand_and_squeeze(_np_a, axes):
np_expand_dims = np.__getattribute__("expand_dims")
ns_expand_dims = nps.__getattribute__("expand_dims")
np_squeeze = np.__getattribute__("squeeze")
ns_squeeze = nps.__getattribute__("squeeze")
_ns_a = nps.array(_np_a)
_ns_ins_a = ns_ins.array(_np_a, block_shape=block_shape)
_np_result = np_expand_dims(_np_a, axes)
_ns_result = ns_expand_dims(_ns_a, axes)
_ns_ins_result = ns_expand_dims(_ns_ins_a, axes)
assert np.allclose(_np_result, _ns_result.get())
assert np.allclose(_np_result, _ns_ins_result.get())
check_dim(_np_result, _ns_result)
check_dim(_np_result, _ns_ins_result)
_np_result = np_squeeze(_np_a)
_ns_result = ns_squeeze(_ns_a)
_ns_ins_result = ns_squeeze(_ns_ins_a)
assert np.allclose(_np_result, _ns_result.get())
assert np.allclose(_np_result, _ns_ins_result.get())
check_dim(_np_result, _ns_result)
check_dim(_np_result, _ns_ins_result)
def test_stack(nps_app_inst):
import nums.numpy as nps
assert nps_app_inst is not None
for fname in ["hstack", "vstack", "dstack", "row_stack", "column_stack"]:
nps_func = nps.__getattribute__(fname)
np_func = np.__getattribute__(fname)
a = nps.array((1, 2, 3))
b = nps.array((2, 3, 4))
np_a, np_b = a.get(), b.get()
assert np.allclose(nps_func((a, b)).get(), np_func((np_a, np_b)))
a = nps.array([[1], [2], [3]])
b = nps.array([[2], [3], [4]])
np_a, np_b = a.get(), b.get()
assert np.allclose(nps_func((a, b)).get(), np_func((np_a, np_b)))
def check_basic_broadcast_correctness(
_np_a, _np_b, _a_blockshape=None, _b_blockshape=None
):
ns_a = nps.array(_np_a)
ns_b = nps.array(_np_b)
if _a_blockshape:
ns_a = ns_a.reshape(block_shape=_a_blockshape)
if _b_blockshape:
ns_b = ns_b.reshape(block_shape=_b_blockshape)
for _op in _ops:
np_op = np.__getattribute__(_op)
ns_op = nps.__getattribute__(_op)
_np_result = np_op(_np_a, _np_b)
_ns_result = ns_op(ns_a, ns_b)
assert np.allclose(_np_result, _ns_result.get())
def check_bitwise_error(_name, error):
np_ufunc = np.__getattribute__(_name)
ns_ufunc = nps.__getattribute__(_name)
_np_a = np.random.randn(2, 2)
_np_b = np.random.randn(2, 2)
_ns_a = nps.array(_np_a).touch()
_ns_b = nps.array(_np_b).touch()
message = ""
# Catching expected error message to ensure same error message is raised below.
try:
_np_result = np_ufunc(_np_a, _np_b)
except TypeError as err:
message = err.args[0]
assert message
with pytest.raises(error, match=message):
_ns_result = ns_ufunc(_ns_a, _ns_b)
def test_average(nps_app_inst):
from nums import numpy as nps
from nums.numpy import BlockArray
assert nps_app_inst is not None
bas = [
nps.array([
[[5, -2, 4, 8], [1, 2, 3, 4], [3, 2, 1, 0], [-1, -2, -3, 0]],
[[6, -4, 2, 7], [4, 3, 2, 1], [1, 2, 0, 3], [0, -2, -3, -1]],
]),
nps.array([
[[5, -2, 4, 8], [1, 2, 3, 4], [3, 2, 1, 0], [-1, -2, -3, 0]],
[[6, -4, 2, 7], [4, 3, 2, 1], [1, 2, 0, 3], [0, -2, -3, -1]],
]),
]
ba_wts = [
None,
nps.array([
[[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]],
[
[-2, -3, -4, -5],
[-2, -3, -4, -5],
[-2, -3, -4, -5],
[-2, -3, -4, -5],
],
]),
]
ba_shapes = [(1, 1, 1), (1, 2, 2), (2, 1, 4), (2, 4, 2), (2, 4, 4)]
for ba, ba_wt in zip(bas, ba_wts):
for ba_shape in ba_shapes:
ba = ba.reshape(block_shape=ba_shape)
if ba_wt:
ba_wt = ba_wt.reshape(block_shape=ba_shape)
np_arr = ba.get()
np_wt = ba_wt.get() if ba_wt else None
op_params = ["average"]
axis_params = [None, 0, 1, 2]
for op, axis in itertools.product(op_params, axis_params):
ns_op = nps.__getattribute__(op)
np_op = np.__getattribute__(op)
np_result = np_op(np_arr,
axis=axis,
weights=np_wt,
returned=True)
ba_result: BlockArray = ns_op(ba,
axis=axis,
weights=ba_wt,
returned=True)
np_avg, np_ws = np_result[0], np_result[1]
ba_avg, ba_ws = ba_result[0], ba_result[1]
assert np.allclose(ba_ws.get(), np_ws)
assert ba_avg.grid.grid_shape == ba_avg.blocks.shape
assert ba_avg.shape == np_avg.shape
assert np.allclose(ba_avg.get(), np_avg)
# Test zero division error
ba = nps.array([
[[5, -2, 4, 8], [1, 2, 3, 4], [3, 2, 1, 0], [-1, -2, -3, 0]],
[[6, -4, 2, 7], [4, 3, 2, 1], [1, 2, 0, 3], [0, -2, -3, -1]],
])
ba_wt = nps.array([
[[1, 2, 3, 4], [1, 2, 3, -12], [1, 2, 3, 4], [1, 2, 3, 4]], # axis 1
[
[-2, -3, 10, -5], # axis 2
[-2, -3, -4, -5],
[-2, -2, -4, -5], # axis 0
[-2, -3, -4, -5],
],
])
for ax in range(3):
err_match = True
try:
np.average(ba.get(), axis=ax, weights=ba_wt.get(), returned=False)
err_match = False
except ZeroDivisionError:
pass
try:
nps.average(ba, axis=ax, weights=ba_wt, returned=False)
err_match = False
except ZeroDivisionError:
pass
assert err_match
def check_dim(_np_a, _ns_a):
np_ndim = np.__getattribute__('ndim')
ns_ndim = nps.__getattribute__('ndim')
assert np_ndim(_np_a) == ns_ndim(_ns_a)