def vector_to_scalar_equal_operation(op, v):
"""
Passed Vector values
[ {(name, tag) : value}, {(name, tag) : value} ...
Return boolean scalar result True if all values are equal
"""
if not op or not v or not isinstance(v, list):
raise HealthException("Insufficient input for Equal operation ")
i0 = v[0]
k1, v1 = get_kv(i0)
v1 = get_value_from_health_internal_tuple(v1)
if v1 and isinstance(v1, list):
v1 = sorted(v1)
for i in v[1:]:
k2, v2 = get_kv(i)
v2 = get_value_from_health_internal_tuple(v2)
if v2 and isinstance(v2, list):
v2 = sorted(v2)
if not op(v1, v2):
return False
return True
def _operate_each_key(self, arg1, arg2, save_param=None):
if isinstance(arg1, dict) and isinstance(arg2, dict):
return None
if not isinstance(arg1, dict) and not isinstance(arg2, dict):
try:
raw_arg1 = get_value_from_health_internal_tuple(arg1)
raw_arg2 = get_value_from_health_internal_tuple(arg2)
if self.op == operator.div and raw_arg2 == 0:
val_to_save = create_value_list_to_save(save_param,
value=0,
op1=arg1,
op2=arg2)
return (0, val_to_save)
# if any of the arg is type float or operation is division
# cast all argument to float
if self.op == operator.div or isinstance(
raw_arg1, float) or isinstance(raw_arg2, float):
raw_arg1 = float(raw_arg1)
raw_arg2 = float(raw_arg2)
result = self.op(raw_arg1, raw_arg2)
val_to_save = create_value_list_to_save(save_param,
value=result,
op1=arg1,
op2=arg2)
return create_health_internal_tuple(result, val_to_save)
except Exception:
return create_health_internal_tuple(None, [])
dict_first = True
if isinstance(arg1, dict):
d = arg1
v = arg2
elif isinstance(arg2, dict):
d = arg2
v = arg1
dict_first = False
res_dict = {}
for _k in d:
if dict_first:
res_dict[_k] = self._operate_each_key(d[_k],
v,
save_param=save_param)
else:
res_dict[_k] = self._operate_each_key(v,
d[_k],
save_param=save_param)
return res_dict
def operate(self,
data={},
check_val=create_health_internal_tuple(True, []),
error=None,
category=None,
level=None,
description=None,
success_msg=None):
if not data:
raise HealthException("Wrong Input Data for ASSERT operation.")
res = {}
res[AssertResultKey.FAIL_MSG] = str(error)
res[AssertResultKey.DESCRIPTION] = description
res[AssertResultKey.SUCCESS_MSG] = success_msg
res[AssertResultKey.KEYS] = []
try:
res[AssertResultKey.CATEGORY] = category.upper().split(".")
except Exception:
res[AssertResultKey.CATEGORY] = None
res[AssertResultKey.LEVEL] = level
if not isinstance(data, dict):
if not self.op(get_value_from_health_internal_tuple(data),
get_value_from_health_internal_tuple(check_val)):
return (ParserResultType.ASSERT, res)
return None
kv = find_kv_vector(NOKEY, data, recurse=True, update_saved_list=False)
if not kv:
return (ParserResultType.ASSERT, res)
fail = False
for i in kv:
k, v = get_kv(i)
kv_tuple = (k, None)
value_to_check = get_value_from_health_internal_tuple(v)
if v[1]:
kv_tuple = (k, v[1])
if not self.op(value_to_check,
get_value_from_health_internal_tuple(check_val)):
res[AssertResultKey.SUCCESS] = False
fail = True
res[AssertResultKey.KEYS].append(kv_tuple)
if not fail:
res[AssertResultKey.SUCCESS] = True
return (ParserResultType.ASSERT, res)
def test_get_value_from_health_internal_tuple(self):
self.assertEqual(
util.get_value_from_health_internal_tuple(
(1, [('conf2', 100, True), ('conf1', 6.0, True)])), 1,
"get_value_from_health_internal_tuple did not return the expected result"
)
self.assertEqual(
util.get_value_from_health_internal_tuple(9), 9,
"get_value_from_health_internal_tuple did not return the expected result"
)
self.assertEqual(
util.get_value_from_health_internal_tuple(None), None,
"get_value_from_health_internal_tuple did not return the expected result"
)
def _operate_each_key(self, arg1, arg2, save_param=None):
if isinstance(arg1, dict):
return None
try:
raw_arg1 = get_value_from_health_internal_tuple(arg1)
raw_arg2 = get_value_from_health_internal_tuple(arg2)
result = self.op(raw_arg1, raw_arg2)
val_to_save = create_value_list_to_save(save_param,
value=result,
op1=arg1,
op2=arg2)
return create_health_internal_tuple(result, val_to_save)
except Exception:
return create_health_internal_tuple(None, [])
def vector_to_scalar_value_uniform_operation(op, v):
"""
Passed Vector values
[ {(name, tag) : value}, {(name, tag) : value} ...
Return boolean scalar result True if all values are uniformly distributed
"""
if not v or not isinstance(v, list):
raise HealthException(
"Insufficient input for Value Uniform operation ")
d = {}
for i in v:
k2, v2 = get_kv(i)
v2 = get_value_from_health_internal_tuple(v2)
if v2 and isinstance(v2, list):
v2 = sorted(v2)
if v2 not in d:
d[v2] = 1
else:
d[v2] = d[v2] + 1
minv = min(d.values())
maxv = max(d.values())
if (maxv - minv) > 1:
return False
return True
def vector_to_vector_no_match_operation(kv, op, a, save_param):
"""
Passed Vector values
[ {(name, tag) : value}, {(name, tag) : value} ...
Return health internal tuple
(True/False , [(key, value, formatting), (key, value, formatting), ...])
"""
res = {}
operand = get_value_from_health_internal_tuple(a)
if not kv:
raise HealthException("Insufficient input for NO_MATCH operation ")
try:
values = [
get_value_from_health_internal_tuple(get_kv(m)[1]) for m in kv
]
match_operand = _find_match_operand_value(operand, values)
result = False
val_to_save = []
for x in kv:
k, v = get_kv(x)
_val = get_value_from_health_internal_tuple(v)
if not op(_val, match_operand):
result |= True
val_to_save += create_value_list_to_save(save_param=None,
value=result,
op1=v)
if operand and operand == MAJORITY:
key = "Majority Value"
else:
key = "Expected Value"
val_to_save += create_value_list_to_save(save_param=save_param,
key=key,
value=match_operand)
res = create_health_internal_tuple(result, val_to_save)
except Exception:
res = create_health_internal_tuple(False, None)
return res
def _operate_each_key(self, arg1, arg2, comp_op=None):
if isinstance(arg1, dict):
return None
if not isinstance(arg2, dict):
try:
raw_arg1 = get_value_from_health_internal_tuple(arg1)
raw_arg2 = get_value_from_health_internal_tuple(arg2)
return comp_op(raw_arg1, raw_arg2)
except Exception:
return False
res_list = []
for _k in arg2:
res_list.append(
self._operate_each_key(arg1, arg2[_k], comp_op=comp_op))
return self.op(res_list)
def vector_to_scalar_first_operation(op, v):
"""
Passed Vector values
[ {(name, tag) : value}, {(name, tag) : value} ...
Returns first value from values
"""
if not v or not isinstance(v, list):
raise HealthException("Insufficient input for Random operation ")
try:
return get_value_from_health_internal_tuple(get_kv(v[0])[1])
except Exception:
return None
def basic_vector_to_scalar_operation(op, kv, typecast=int, initial_value=None):
"""
Passed Vector values and type of value
[ {(name, tag) : value}, {(name, tag) : value} ...
Return aggregated result along with count of element processed
"""
if not op or not kv or not isinstance(kv, list):
raise HealthException("Insufficient input for vector operation ")
if initial_value:
found_first = True
res = initial_value
else:
found_first = False
res = None
cnt = 0
for i in kv:
k1, v1 = get_kv(i)
v1 = get_value_from_health_internal_tuple(v1)
try:
if not found_first:
res = typecast(v1)
found_first = True
cnt = cnt + 1
continue
except Exception:
continue
try:
res = op(res, v1)
cnt = cnt + 1
except Exception:
continue
return res, cnt
def vector_to_vector_sd_anomaly_operation(kv, op, a, save_param):
"""
Passed Vector values
[ {(name, tag) : value}, {(name, tag) : value} ...
Return health internal tuple
(True/False , [(key, value, formatting), (key, value, formatting), ...])
"""
res = {}
sd_multiplier = get_value_from_health_internal_tuple(a)
if not kv or not sd_multiplier:
raise HealthException("Insufficient input for SD_ANOMALY operation ")
try:
n = len(kv)
if n < 3:
no_anomaly = True
range_start = 0
range_end = 0
else:
values = [
get_value_from_health_internal_tuple(get_kv(m)[1]) for m in kv
]
no_anomaly = False
try:
# We should consider int and floats only
s = sum(values)
except Exception:
no_anomaly = True
if not no_anomaly:
mean = float(s) / float(n)
variance = 0
for v in values:
variance += pow((v - mean), 2)
variance = float(variance) / float(n)
sd = sqrt(variance)
range_start = mean - (sd_multiplier * sd)
range_end = mean + (sd_multiplier * sd)
result = False
val_to_save = []
for x in kv:
k, v = get_kv(x)
_val = get_value_from_health_internal_tuple(v)
if not no_anomaly and (float(_val) < float(range_start)
or float(_val) > float(range_end)):
result |= True
val_to_save += create_value_list_to_save(save_param=None,
value=result,
op1=v)
val_to_save += create_value_list_to_save(save_param=save_param,
value=result)
res = create_health_internal_tuple(result, val_to_save)
except Exception:
res = create_health_internal_tuple(False, None)
return res
def vector_to_vector_diff_operation(kv, op, a, save_param):
"""
Passed Vector values
[ {(name, tag) : value}, {(name, tag) : value} ...
Return boolean dictionary result
{ (name, tag) : True/False , (name, tag) : True/False, ... }
"""
res = {}
temp_res = {}
if not kv or not a:
raise HealthException("Insufficient input for Diff operation ")
exception_found = False
try:
for x, y in itertools.combinations(kv, 2):
k1, v1 = get_kv(x)
k2, v2 = get_kv(y)
_v1 = get_value_from_health_internal_tuple(v1)
_v2 = get_value_from_health_internal_tuple(v2)
if op(abs(_v1 - _v2), a):
try:
temp_res[make_key(k1)] |= True
except Exception:
temp_res[make_key(k1)] = True
try:
temp_res[make_key(k2)] |= True
except Exception:
temp_res[make_key(k2)] = True
else:
try:
temp_res[make_key(k1)] |= False
except Exception:
temp_res[make_key(k1)] = False
try:
temp_res[make_key(k2)] |= False
except Exception:
temp_res[make_key(k2)] = False
for i in kv:
k, v = get_kv(i)
val_to_save = create_value_list_to_save(
save_param, value=temp_res[make_key(k)], op1=v)
res[make_key(k)] = create_health_internal_tuple(
temp_res[make_key(k)], val_to_save)
except Exception:
exception_found = True
if exception_found:
for x in kv:
k, v = get_kv(x)
res[make_key(k)] = create_health_internal_tuple(None, None)
return res
