def test_get_scoop_simulate_muon_rings_path():
dme = edM.DetectionMethodEvaluation(output_dir, number_of_muons, steps,
step_size, scoop_hosts)
path_to_scoop_simulate = dme.get_scoop_simulate_muon_rings_path()
existing = False
if os.path.exists(path_to_scoop_simulate):
existing = True
assert existing == True
def test_create_outputDir():
dme = edM.DetectionMethodEvaluation(output_dir, number_of_muons, steps,
step_size, scoop_hosts)
dme.create_outputDir()
existing = False
if os.path.isdir(output_dir):
existing = True
assert existing == True
def test_check_input_correctness_noScoopHosts():
try:
dme = edM.DetectionMethodEvaluation(output_dir, number_of_muons, steps,
step_size, "/foo/bar")
dme.check_input_correctness()
error = False
except ValueError:
error = True
assert error == True
def test_check_input_correctness_wrong_stepSize():
try:
dme = edM.DetectionMethodEvaluation(output_dir, number_of_muons, steps,
"small", scoop_hosts)
dme.check_input_correctness()
error = False
except ValueError:
error = True
assert error == True
def test_check_input_correctness_wrong_muonNumber2():
try:
dme = edM.DetectionMethodEvaluation(output_dir, "5", steps, step_size,
scoop_hosts)
dme.check_input_correctness()
error = False
except ValueError:
error = True
assert error == True
def test_one_nsb_rate():
dme = edM.DetectionMethodEvaluation(output_dir, 100, steps, step_size,
scoop_hosts)
dme.one_nsb_rate(output_dir, 35e6)
wild_card_path = os.path.join(output_dir, "*")
i = 0
for path in glob.glob(wild_card_path):
i += 1
assert i == 4
def test_save_to_file():
dme = edM.DetectionMethodEvaluation(output_dir, number_of_muons, steps,
step_size, scoop_hosts)
events = [[3, 3, 2, 1, 1, 1, 200 / 3, 200 / 3],
[3, 3, 2, 1, 1, 1, 200 / 3, 200 / 3]]
file_out = os.path.join(output_dir, "nsb_35e6_precision_results.csv")
dme.save_to_file(file_out, events)
statinfo = os.stat(file_out)
assert os.path.exists(file_out) and statinfo.st_size > 112
def test_run_simulation():
dme = edM.DetectionMethodEvaluation(output_dir, number_of_muons, steps,
step_size, scoop_hosts)
dme.run_simulation(simulation_dir)
dir_wild_card_path = os.path.join(simulation_dir, "*")
i = 0
for path in glob.glob(dir_wild_card_path):
i += 1
assert i == 2
def test_plot_different_NSB():
dme = edM.DetectionMethodEvaluation(output_dir, 10, steps, step_size,
scoop_hosts)
NSB_rates = [1, 2, 3]
precisions = [99, 98, 97]
sensitivities = [100, 50, 80]
muonCounts = [12, 12, 12]
dme.plot_different_NSB(NSB_rates, precisions, sensitivities, muonCounts)
number_of_elements = len(os.listdir(output_dir))
assert number_of_elements == 2
def test_plot_sensitivity_precision():
dme = edM.DetectionMethodEvaluation(output_dir, number_of_muons, steps,
step_size, scoop_hosts)
results = {
"avg_precision": 90,
"std_precision": 10,
"avg_sensitivity": 90,
"std_sensitivity": 10,
"precisions": np.array([80, 100]),
"sensitivities": np.array([80, 100])
}
dme.plot_sensitivity_precision(results, output_dir)
path = os.path.join(output_dir, "sensitivity.png")
path2 = os.path.join(output_dir, "precision.png")
assert os.path.exists(path) and os.path.exists(path2)
def test_true_false_decisions():
dme = edM.DetectionMethodEvaluation(output_dir, 2, steps, step_size,
scoop_hosts)
p1 = np.array([1, 1, 2])
p2 = np.array([1, 1, 1])
p3 = np.array([2, 2, 2])
p4 = np.array([4, 4, 4])
p5 = np.array([5, 5, 5])
all_photons_run = np.array(
[np.array([p1, p2, p3, p4, p5]),
np.array([p1, p2, p3, p4, p5])])
pure_cherenkov_run_photons = np.array(
[np.array([p2, p3, p4]),
np.array([p2, p3, p4])])
nsb_run_found_photons = np.array(
[np.array([p1, p3, p4]),
np.array([p1, p3, p4])])
events = dme.true_false_decisions(all_photons_run,
pure_cherenkov_run_photons,
nsb_run_found_photons)
np.testing.assert_almost_equal(events,
[[3, 3, 2, 1, 1, 1, 200 / 3, 200 / 3],
[3, 3, 2, 1, 1, 1, 200 / 3, 200 / 3]], 5)
def test_multiple_nsb_rates():
dme = edM.DetectionMethodEvaluation(output_dir, 10, 5, 1.05, scoop_hosts)
dme.multiple_nsb_rates()
number_of_elements = len(os.listdir(output_dir))
assert number_of_elements == 7
def test_analyze():
dme = edM.DetectionMethodEvaluation(output_dir, number_of_muons, steps,
step_size, scoop_hosts)
file_out = os.path.join(output_dir, "nsb_35e6_precision_results.csv")
results = dme.analyze(file_out)
np.testing.assert_almost_equal(results['avg_precision'], 200 / 3, 5)
def test_do_clustering2():
dme = edM.DetectionMethodEvaluation(output_dir, number_of_muons, steps,
step_size, scoop_hosts)
results = dme.do_clustering(simulationFile, simulationFile)
assert len(results[0]) != 0