def test_print_methods():
"""Test method."""
set_opt("verbose", True)
f_stream = io.StringIO()
with redirect_stdout(f_stream):
nb_print("status")
nb_data_wait("table1")
check.is_in("status", str(f_stream.getvalue()))
check.is_in("Getting data from table1", str(f_stream.getvalue()))
set_opt("verbose", False)
f_stream = io.StringIO()
with redirect_stdout(f_stream):
nb_print("status")
check.is_not_in("status", str(f_stream.getvalue()))
check.is_not_in("Getting data from table1", str(f_stream.getvalue()))
set_opt("debug", True)
f_stream = io.StringIO()
with redirect_stdout(f_stream):
nb_debug("debug", "debugmssg", "val", 1, "result", True)
check.is_in("debug", str(f_stream.getvalue()))
check.is_in("debugmssg", str(f_stream.getvalue()))
check.is_in("val", str(f_stream.getvalue()))
check.is_in("1", str(f_stream.getvalue()))
check.is_in("result", str(f_stream.getvalue()))
check.is_in("True", str(f_stream.getvalue()))
def test_simple_aperture(r):
data = np.ones((10, 10))
res = aperture_photometry(data, [5], [5], r=r, r_ann=None)
npt.assert_array_equal(res['x'], [5])
npt.assert_array_equal(res['y'], [5])
npt.assert_array_equal(res['aperture'], [r])
npt.assert_almost_equal(res['flux'], [np.pi * r**2])
check.is_not_in('sky', res.colnames)
npt.assert_array_almost_equal(res['flux_error'], [np.sqrt(np.pi * r**2)])
npt.assert_array_equal(res['flags'], [0])
def testLenght(self):
x = len('nameTEST')
a = 1
b = 100
c = range(2, 101)
d = 102
check.greater(x, a)
check.less_equal(x, b)
check.is_in(x, c, "Lenght ok")
check.is_not_in(d, c, "Lenght not ok")
def test_extract_header_nosip():
header = fits.Header.fromstring(_base_header + _wcs_no_sip, sep='\n')
h, wcs = extract_header_wcs(header)
check.is_instance(wcs, WCS)
check.equal(wcs.wcs.ctype[0], 'RA---TAN')
check.equal(wcs.wcs.ctype[1], 'DEC--TAN')
check.is_instance(h, fits.Header)
for i in _comon_wcs_keys:
check.is_not_in(f'{i}1', h.keys())
check.is_not_in(f'{i}2', h.keys())
check.is_in('DATE-OBS', h.keys())
check.is_false(h is header)
check.not_equal(h, header)
def test_new_init_data_providers(monkeypatch):
"""Test creating new provider with new provider list."""
monkeypatch.setattr(data_providers, "GeoLiteLookup", GeoIPLiteMock)
data_providers.init(query_provider="LocalData", providers=[])
dprov = data_providers.DataProviders.current()
data_providers.init(query_provider="LocalData", providers=[])
dprov2 = data_providers.DataProviders.current()
check.equal(dprov2, dprov)
# specify provider
dprov = data_providers.DataProviders(query_provider="LocalData")
data_providers.init(query_provider="LocalData", providers=["tilookup"])
msticnb = sys.modules["msticnb"]
dprov2 = data_providers.DataProviders.current()
pkg_providers = getattr(msticnb, "data_providers")
check.not_equal(dprov2, dprov)
check.is_in("LocalData", dprov2.providers)
check.is_in("tilookup", dprov2.providers)
check.is_not_in("geolitelookup", dprov2.providers)
check.is_not_in("ipstacklookup", dprov2.providers)
check.is_in("LocalData", pkg_providers)
check.is_in("tilookup", pkg_providers)
check.is_not_in("geolitelookup", pkg_providers)
check.is_not_in("ipstacklookup", pkg_providers)
check.is_instance(dprov2.providers["tilookup"], TILookup)
def test_classify_img():
image_dir = '/sampleImage/'
# Changes to tests:
# we changed the images classified due to the limitations of our ML model
# since the model is only trained on 1000 object categories
# we will create a helper function in the next iteration
# to test for semantic simularity and get better search results
image_names = [
'banana',
'basketball',
'carton',
'cucumber',
'fountain',
'golden_retriever',
'goldfish',
'passenger_car',
'pop_bottle',
'seashore',
'space_shuttle',
'sports_car',
'suit',
'tabby',
'volcano'
]
# Changes to tests:
# wrong_names is a rotation of original image_names
# as it is unlikely that basketball
# will be in the classification dict for banana and so on
wrong_names = image_names[1:] + image_names[:1]
img_ext = '.jpg'
# instead of simply using os.getcwd(), we use full_path so that pytest will
# find the images regardless of where you run pytest
# (like in test/ as opposed to main dir)
full_path = os.path.realpath(__file__)
test_folder = os.path.dirname(full_path)
w = Worker()
check.is_none(w.classify_img(None))
for idx, name in enumerate(image_names):
img = Image.open(test_folder + image_dir + name + img_ext)
# should all be true
# (that 'banana' is in classification dict for 'banana.jpg' and so on)
check.is_in(name, w.classify_img(img))
# now let's try assertions that should definitely be wrong
# (that 'volcano' is in the classification dict for 'banana.jpg')
check.is_not_in(wrong_names[idx], w.classify_img(img))
def test_logger_remove_handler():
mylog = logger.getChild('testing')
msg = 'Some error happend here.'
logs = []
lh = log_to_list(mylog, logs)
mylog.setLevel('DEBUG')
mylog.error(msg)
check.is_instance(lh, ListHandler)
check.is_in(lh, mylog.handlers)
mylog.removeHandler(lh)
check.is_not_in(lh, mylog.handlers)
check.equal(logs[0], msg)
check.equal(lh.log_list[0], msg)
check.equal(lh.log_list, logs)
def test_get_related_words():
w = Worker()
originals = [name.replace(' ', '_') for name in image_names]
wrong_names = originals[1:] + originals[:1]
check.equal({}, w.get_related_words(''))
for idx, name in enumerate(originals):
related_set = w.get_related_words(name)
# should all be true
# (that 'plantain' is in related words set for 'banana' and so on)
check.is_in(related[idx], related_set)
# now let's try assertions that should definitely be wrong
# (that 'lava' is in the related words set for 'banana')
check.is_not_in(wrong_names[idx], related_set)
def test_pd_display(test_df, capsys):
"""Test mp_pivot.display accessor."""
test_df.mp_pivot.display(
_nop_df,
cols=["Computer", "SubjectUserName", "TargetUserName"],
query="TargetUserName != 'MSTICAdmin'",
)
cap_out = capsys.readouterr().out
check.is_in("Computer SubjectUserName TargetUserName", cap_out)
check.is_not_in("MSTICAdmin", cap_out)
check.equal(Counter(cap_out.split())["MSTICAlertsWin1"], 12)
check.equal(len(cap_out.split("\n")), len(test_df) + 1)
test_df.mp_pivot.display(
_nop_df,
cols=["Computer", "SubjectUserName", "TargetUserName"],
query="TargetUserName != 'MSTICAdmin'",
head=5,
)
cap_out = capsys.readouterr().out
check.is_in("Computer SubjectUserName TargetUserName", cap_out)
check.equal(Counter(cap_out.split())["MSTICAlertsWin1"], 5)
def test_add_sub_data_providers(monkeypatch):
"""Test intializing adding and subtracting providers."""
monkeypatch.setattr(data_providers, "GeoLiteLookup", GeoIPLiteMock)
dprov = data_providers.DataProviders(query_provider="LocalData")
data_providers.init(query_provider="LocalData", providers=["tilookup"])
msticnb = sys.modules["msticnb"]
dprov2 = data_providers.DataProviders.current()
# Add and remove a provider from defaults
data_providers.init(
query_provider="LocalData", providers=["+ipstacklookup", "-geolitelookup"]
)
dprov3 = data_providers.DataProviders.current()
pkg_providers = getattr(msticnb, "data_providers")
check.not_equal(dprov3, dprov)
check.not_equal(dprov3, dprov2)
check.is_in("ipstacklookup", dprov3.providers)
check.is_not_in("geolitelookup", dprov3.providers)
check.is_in("tilookup", dprov3.providers)
check.is_in("ipstacklookup", pkg_providers)
check.is_not_in("geolitelookup", pkg_providers)
check.is_in("tilookup", pkg_providers)
def test_is_not_in():
check.is_not_in(4, [1, 2, 3])
def test_pivot_funcs_df_merge(_create_pivot, join_type, test_case):
"""Test calling function with DF input attributes."""
func = getattr(getattr(test_case.entity, test_case.provider), test_case.pivot_func)
# Test DF input
val = test_case.value
in_df = pd.DataFrame(val, columns=[test_case.src_df_col])
params = {test_case.func_param: test_case.src_df_col}
in_df["extra_col1"] = "test1"
in_df["extra_col2"] = "test2"
result_no_merge_df = func(data=in_df, **params)
if test_case.entity not in (entities.Account, entities.Host):
# The IP test uses a list param so we cannot do index joins
# with it
with pytest.warns(UserWarning):
result_df = func(data=in_df, **params, join=join_type)
return
# should work ok with Account and Host
result_df = func(data=in_df, **params, join=join_type)
in_cols = in_df.shape[1]
no_merge_cols = result_no_merge_df.shape[1]
merge_cols = result_df.shape[1]
# merged DF should have result + input cols - join key col
check.greater_equal(no_merge_cols + in_cols, merge_cols)
if join_type in ("left", "inner"):
# inner and left joins should have same or greater length as input
check.greater_equal(result_df.shape[0], in_df.shape[0])
# all the keys from the input should be in the merged output
for row_val in in_df[test_case.src_df_col]:
check.is_in(row_val, result_df[test_case.src_df_col].values)
if join_type == "right":
# We don't know how many results we get back from right join
# (although should not be zero)
check.greater(len(result_df), 0)
# but all of its key values should be present in input
for row_val in result_df[test_case.src_df_col].values:
check.is_in(row_val, in_df[test_case.src_df_col].values)
join_in_data = {
0: "0x3e7",
1: "0xc90e957",
2: "0xc90ea44",
3: "0xc912d62",
4: "0xc913737",
10: "0x3e3",
14: "0x3e4",
15: "0xaddd",
16: "0xafff",
17: "0x3e5",
23: "no_match",
}
in_df = pd.DataFrame(
pd.Series(join_in_data), columns=["TargetLogonId"]
).reset_index()
result_no_merge_df = func(data=in_df, **params)
result_df = func(
data=in_df,
**params,
join=join_type,
left_on="TargetLogonId",
right_on="TargetLogonId",
)
check.is_not_none(result_df)
if join_type in ("inner", "right"):
check.equal(len(result_df), len(result_no_merge_df))
for val in join_in_data.values():
if val != "no_match":
check.is_in(val, result_df["TargetLogonId"].values)
else:
check.is_not_in(val, result_df["TargetLogonId"].values)
if join_type == "left":
check.equal(len(result_df), len(result_no_merge_df) + 1)
for val in join_in_data.values():
check.is_in(val, result_df["TargetLogonId"].values)
def test_external_links():
page = BasePage(TEST_HTML_PAGE, TEST_HTML_URL)
check.is_not_in(TEST_HTML_PAGE, [i.absolute_url for i in page.external_links])
check.is_true(page.external_links)
