def test_transform(self):
aws_burn_rate = AwsBurnRate.AwsBurnRate(config)
res = aws_burn_rate.transform(data_block)
verify_products(aws_burn_rate, res)
expected_df = expected_transform_output["AWS_Burn_Rate"]
res_df = res["AWS_Burn_Rate"]
assert np.isclose(expected_df["BurnRate"], res_df["BurnRate"])
def test_acquire(self):
with mock.patch.object(google.auth, "default") as default:
default.return_value = (None, None)
with mock.patch.object(GceOccupancy.GceOccupancy, "_get_client") as client:
client.return_value = MockClient()
occupancy = GceOccupancy.GceOccupancy(CONFIG)
res = occupancy.acquire()
verify_products(occupancy, res)
assert EXPECTED_DF.equals(res.get("GCE_Occupancy"))
def test_transform():
nersc_figure_of_merit = NerscFigureOfMerit.NerscFigureOfMerit(config)
res = nersc_figure_of_merit.transform(data_block)
verify_products(nersc_figure_of_merit, res)
for key, value in res.items():
try:
assert expected_transform_output[key].equals(value)
except Exception:
print(key, " fail\n", expected_transform_output[key], "\n", value)
def test_acquire():
nersc_job_info = NerscJobInfo.NerscJobInfo(CONFIG)
with mock.patch.object(newt.Newt, "get_status") as get_status:
get_status.return_value = utils.input_from_file(STATUS_FIXTURE_FILE)
with mock.patch.object(newt.Newt, "get_queue") as get_queue:
get_queue.return_value = utils.input_from_file(JOBS_FIXTURE_FILE)
res = nersc_job_info.acquire()
verify_products(nersc_job_info, res)
new_df = res["Nersc_Job_Info"]
new_df = new_df.reindex(EXPECTED_PANDAS_DFRAME.columns, axis=1)
pandas.testing.assert_frame_equal(EXPECTED_PANDAS_DFRAME, new_df)
def test_acquire():
def side_effect_get_usage(username):
if username == FAKE_USER:
return {"items": []}
return utils.input_from_file(ALLOCATIONS_FIXTURE_FILE)
nersc_allocations = NerscAllocationInfo.NerscAllocationInfo(CONFIG)
with mock.patch.object(newt.Newt, "get_usage") as f:
f.side_effect = side_effect_get_usage
res = nersc_allocations.acquire()
verify_products(nersc_allocations, res)
assert EXPECTED_PANDAS_DFRAME.equals(res["Nersc_Allocation_Info"])
def test_transform():
data_block = create_datablock()
fom = FigureOfMerit.FigureOfMerit(data_block)
res = fom.transform(data_block)
verify_products(fom, res)
for k in expected_reply.keys():
if k == "AWS_Price_Performance":
df = fix_column(res[k], "AWS_Price_Performance")
edf = fix_column(expected_reply[k], "AWS_Price_Performance")
else:
df = fix_column(res[k], "AWS_Figure_Of_Merit")
edf = fix_column(expected_reply[k], "AWS_Figure_Of_Merit")
pd.testing.assert_frame_equal(edf, df)
def test_wrong_product_types():
@Source.produces(a=str, b=int)
class AMaker(Source.Source):
def __init__(self, config):
super().__init__(config)
def acquire(self):
return {"a": 42, "b": 17}
maker = AMaker({"channel_name": "test"})
expected_err_msg = "The following products have the wrong types:\n" + r" - 'a' \(expected 'str', got 'int'\)"
with pytest.raises(Exception, match=expected_err_msg):
verify_products(maker, maker.acquire())
def test_wrong_product_names():
@Source.produces(a=str)
class BMaker(Source.Source):
def __init__(self, config):
super().__init__(config)
def acquire(self):
return {'b': ''}
maker = BMaker({})
expected_err_msg = "The following products were not produced:\n" + \
" - 'a' of type 'str'\n\n" + \
"The following products were not declared:\n" + \
" - 'b' of type 'str'"
with pytest.raises(Exception, match=expected_err_msg):
verify_products(maker, maker.acquire())
def test_acquire(self):
aws_i_p = AWSInstancePerformance.AWSInstancePerformance(config)
res = aws_i_p.acquire()
verify_products(aws_i_p, res)
assert expected_pandas_df.equals(res.get("Performance_Data"))
def test_acquire(self):
aws_job_limits = AWSJobLimits.AWSJobLimits(config)
res = aws_job_limits.acquire()
verify_products(aws_job_limits, res)
assert expected_pandas_df.equals(res.get("Job_Limits"))
def test_acquire(self):
gce_price_performance = GCEInstancePerformance.GCEInstancePerformance(
CONFIG)
res = gce_price_performance.acquire()
verify_products(gce_price_performance, res)
assert EXPECTED_PANDAS_DF.equals(res.get('GCE_Instance_Performance'))
def test_acquire(self):
nersc_instance_performance = NerscInstancePerformance.NerscInstancePerformance(CONFIG)
res = nersc_instance_performance.acquire()
verify_products(nersc_instance_performance, res)
assert EXPECTED_PANDAS_DF.equals(res.get('Nersc_Instance_Performance'))
