def test_init():
significance = 0.1
gross = False
energy_bins = 10
analysis = H0(significance=significance,
gross=gross,
energy_bins=energy_bins)
np.testing.assert_equal(analysis.log_significance, np.log10(significance))
np.testing.assert_equal(analysis.gross, gross)
np.testing.assert_equal(analysis.triggers.shape, (0, energy_bins + 1))
def test_zero_counts_gross():
# accept all pvals
significance = 1.0
energy_bins = 10
analysis = H0(significance=significance,
gross=True,
energy_bins=energy_bins)
spectrum1 = np.zeros((10, ))
spectrum2 = np.zeros((10, ))
# run twice for initialization
analysis.run_gross(spectrum1, 1)
analysis.run_gross(spectrum2, 2)
obs = analysis.triggers
exp = np.array([[1.0, 0.0, 0.0, 0.0]])
np.testing.assert_equal(obs, exp)
def test_gross():
stride = 10
integration = 10
# run handler script with analysis parameter
analysis = H0()
classifier = RadClass(stride,
integration,
test_data.datapath,
test_data.filename,
analysis=analysis)
classifier.run_all()
obs_timestamp = analysis.triggers[0][0]
exp_timestamp = timestamps[-(rejected_H0_time + integration)]
np.testing.assert_equal(obs_timestamp, exp_timestamp)
# there should only be one rejected hypothesis
obs_rows = analysis.triggers.shape[0]
exp_rows = 1
np.testing.assert_equal(obs_rows, exp_rows)
def test_write_gross():
stride = 10
integration = 10
filename = 'h0test_gross.csv'
# run handler script with analysis parameter
analysis = H0()
classifier = RadClass(stride,
integration,
test_data.datapath,
test_data.filename,
analysis=analysis)
classifier.run_all()
analysis.write(filename)
results = np.loadtxt(filename, delimiter=',')
# expected shape is only 1D because only 1 entry is expected
obs = results.shape
exp = (4, )
np.testing.assert_equal(obs, exp)
os.remove(filename)
def test_zero_counts_channel():
# accept all pvals
significance = 1.0
energy_bins = 10
analysis = H0(significance=significance,
gross=False,
energy_bins=energy_bins)
spectrum1 = np.zeros((10, ))
spectrum2 = np.zeros((10, ))
spectrum2[0] = 100.0
# run twice for initialization
analysis.run_channels(spectrum1, 1)
analysis.run_channels(spectrum2, 2)
obs_pvals = analysis.triggers[0][1:]
obs_ts = analysis.triggers[0][0]
np.testing.assert_equal(obs_ts, 1)
# only the first channel will have failed
np.testing.assert_equal(np.count_nonzero(obs_pvals), 1)
# check that the first channel alone is rejected
np.testing.assert_equal(np.sum(obs_pvals), obs_pvals[0])
def test_write_channel():
stride = 10
integration = 10
filename = 'h0test_channel.csv'
# run handler script with analysis parameter
analysis = H0(gross=False, energy_bins=test_data.energy_bins)
classifier = RadClass(stride,
integration,
test_data.datapath,
test_data.filename,
analysis=analysis)
classifier.run_all()
analysis.write(filename)
results = np.loadtxt(filename, delimiter=',')
# 1 extra columns are required for timestamp
# expected shape is only 1D because only 1 entry is expected
obs = results.shape
exp = (test_data.energy_bins + 1, )
np.testing.assert_equal(obs, exp)
os.remove(filename)