def test_unique_resource_values(query_profiles):
"""Test 'unique_resource_values_of' on resource.
Expected no exception, all assertions passed.
"""
# Acquire the memory query profile
mem_profile = profile_filter(query_profiles,
'memory-2017-08-25-16-03-47.perf')
assert mem_profile is not None
# Test the searching in first level of hierarchy
unique_values = list(query.unique_resource_values_of(
mem_profile, 'amount'))
unique_values.sort()
assert len(unique_values) == _MEMORY_AMOUNT_COUNT
assert unique_values == _MEMORY_AMOUNT_LIST
# Test the searching in dictionary -> list hierarchy
unique_values = list(
query.unique_resource_values_of(mem_profile, 'trace::function'))
unique_values.sort()
assert len(unique_values) == _MEMORY_TRACE_FUNCTION_COUNT
assert unique_values == _MEMORY_TRACE_FUNCTION_LIST
# Test the searching in dictionaries hierarchy
unique_values = list(
query.unique_resource_values_of(mem_profile, 'uid:line'))
unique_values.sort()
assert len(unique_values) == _MEMORY_UID_LINE_COUNT
assert unique_values == _MEMORY_UID_LINE_LIST
# Test key that is not in the resources
unique_values = list(
query.unique_resource_values_of(mem_profile, 'test:testing::test'))
assert not unique_values
def generate_plot_data_slices(profile):
""" Generates data slices for plotting resources and models. The resources are split by unique
uids, models are sliced into parts by uid and interval.
:param dict profile: loaded perun profile
:returns generator: generator: resources and models slices of unique uid as pair
(data_slice(pandas.DataFrame), uid_models(list))
"""
# Get resources for scatter plot points and models for curves
resource_table = convert.resources_to_pandas_dataframe(profile)
models = list(map(itemgetter(1), query.all_models_of(profile)))
# Get unique uids from profile, each uid (and optionally interval) will have separate graph
uids = map(convert.flatten,
query.unique_resource_values_of(profile, 'uid'))
# Process each uid data
for uid_slice, uid_models in slice_resources_by_uid(
resource_table, models, uids):
# Slice the uid models according to different intervals (each interval is plotted
# separately as it improves readability)
if uid_models:
for interval_models in slice_models_by_interval(uid_models):
yield uid_slice, interval_models
else:
# There are no models to plot
yield uid_slice, []
def test_unique_model_values(query_profiles):
"""Test 'unique_model_values_of' on model.
Expected no exception, all assertions passed.
"""
# Acquire the models query profile
models_profile = profile_filter(query_profiles, 'complexity-models.perf')
assert models_profile is not None
# Test the searching in first level of hierarchy
unique_values = list(query.unique_model_values_of(models_profile, 'model'))
unique_values.sort()
assert len(unique_values) == _MODELS_MODEL_COUNT
assert unique_values == _MODELS_MODEL_LIST
# Test the searching in dictionary -> list hierarchy
unique_values = list(
query.unique_model_values_of(models_profile, 'coeffs::name'))
unique_values.sort()
assert len(unique_values) == _MODELS_COEFFS_NAME_COUNT
assert unique_values == _MODELS_COEFFS_NAME_LIST
# Test key that is not in the models
unique_values = list(
query.unique_resource_values_of(models_profile, 'test'))
assert not unique_values