def test_perf():
"""
Performances measurement on full EDF
"""
# Prepare list of TS
ts_list_origin = IkatsApi.ds.read(ds_name="EDF")["ts_list"]
md_list = IkatsApi.md.read(ts_list=ts_list_origin)
results = None
ts_start = 0
ts_end = len(ts_list_origin) + 1
for i in range(ts_start + 1, ts_end):
for save in [False, True]:
ts_list = ts_list_origin[ts_start:i]
time_before = time.time()
# Compute
results = compute_slope(ts_list=ts_list, save_new_ts=save)
time_after = time.time()
nb_points = sum([
int(md_list[x]['qual_nb_points']) for x in md_list
if x in ts_list
])
print(
"PERF %s TS, %s pts, save=%s in %ss (speed: %.2f pts/s)" %
(len(ts_list), nb_points, save,
(time_after - time_before), nb_points /
(time_after - time_before)))
print(results)
def test_slope_same_ts(self):
"""
Compute the slope of a 2 TS
"""
# Defining default results (for cleanup purposes)
results = None
# Prepare list of identical TS
ts_item = gen_ts(1)
ts_list = [ts_item, ts_item]
try:
# Compute
results = compute_slope(ts_list=ts_list)
# Get computed data
obtained_timeseries_data_1 = IkatsApi.ts.read(
tsuid_list=[results[0]['tsuid']])[0]
obtained_timeseries_data_2 = IkatsApi.ts.read(
tsuid_list=[results[1]['tsuid']])[0]
# Same number of TS generated
self.assertEqual(len(ts_list), len(results))
# Computed TSUID are identical (because they have the same functional identifier)
self.assertEqual(results[0]['tsuid'], results[1]['tsuid'])
# Compare values
self.assertTrue(
np.allclose(np.array(expected_ts(1), dtype=np.float64),
np.array(obtained_timeseries_data_1,
dtype=np.float64),
atol=1e-2))
self.assertTrue(
np.allclose(np.array(expected_ts(1), dtype=np.float64),
np.array(obtained_timeseries_data_2,
dtype=np.float64),
atol=1e-2))
finally:
for timeseries in ts_list:
IkatsApi.ts.delete(timeseries['tsuid'], no_exception=True)
if results is not None:
for timeseries in results:
IkatsApi.ts.delete(timeseries['tsuid'], no_exception=True)
def test_slope_simple(self):
"""
Compute the slope of a simple TS
* Going up
* Going down
* Constant
"""
# Defining default results (for cleanup purposes)
results = None
# Prepare list of TS
ts_list = [gen_ts(1)]
try:
# Compute
results = compute_slope(ts_list=ts_list)
# Get computed data
obtained_timeseries_data = IkatsApi.ts.read(
tsuid_list=[results[0]['tsuid']])[0]
# Same number of TS generated
self.assertEqual(len(ts_list), len(results))
# Compare length
self.assertEqual(len(expected_ts(1)),
len(obtained_timeseries_data))
# Compare values
self.assertTrue(
np.allclose(np.array(expected_ts(1), dtype=np.float64),
np.array(obtained_timeseries_data,
dtype=np.float64),
atol=1e-2))
finally:
for timeseries in ts_list:
IkatsApi.ts.delete(timeseries['tsuid'], no_exception=True)
if results is not None:
for timeseries in results:
IkatsApi.ts.delete(timeseries['tsuid'], no_exception=True)
def test_slope_special_chunk(self):
"""
Compute the slope of a TS having chunks containing:
* no points
* 1 single point
* several points
* last chunk has one point
"""
# Defining default results (for cleanup purposes)
results = None
# Prepare list of TS
ts_list = [gen_ts(3)]
try:
# Compute
results = compute_slope(ts_list=ts_list, chunk_size=4)
# Get computed data
obtained_timeseries_data = IkatsApi.ts.read(
tsuid_list=[results[0]['tsuid']])[0]
# Same number of TS generated
self.assertEqual(len(ts_list), len(results))
# Compare values
self.assertTrue(
np.allclose(np.array(expected_ts(3), dtype=np.float64),
np.array(obtained_timeseries_data,
dtype=np.float64),
atol=1e-2))
finally:
for timeseries in ts_list:
IkatsApi.ts.delete(timeseries['tsuid'], no_exception=True)
if results is not None:
for timeseries in results:
IkatsApi.ts.delete(timeseries['tsuid'], no_exception=True)