def test_peak_width(self):
self.assertRaises(AssertionError, cet, test_data, 1, -1)
expected_results = numpy.array([1.0, 1.2, 1.3])
results = cet(test_data, 2, 0.021)
print 'testing %s expected to be %s' % (results, expected_results)
self.assertEqual(expected_results.shape, results.shape)
self.assertTrue(numpy.equal(expected_results, results).all())
print 'passed'
# 1.0 not on three channels, but two, even though it has three
# elements within 0.2 sec of one another in flattened list.
expected_results = numpy.array([1.2, 1.3])
results = cet(test_data, 3, 0.021)
print 'testing %s expected to be %s' % (results, expected_results)
self.assertEqual(expected_results.shape, results.shape)
self.assertTrue(numpy.equal(expected_results, results).all())
print 'passed'
# with larger peak_width
expected_results = numpy.array([1.0])
results = cet(test_data, 3, 0.4)
print 'testing %s expected to be %s' % (results, expected_results)
self.assertEqual(expected_results.shape, results.shape)
self.assertTrue(numpy.equal(expected_results, results).all())
print 'passed'
# with medium peak_width
expected_results = numpy.array([1.0, 1.3])
results = cet(test_data, 3, 0.25)
print 'testing %s expected to be %s' % (results, expected_results)
self.assertEqual(expected_results.shape, results.shape)
self.assertTrue(numpy.equal(expected_results, results).all())
print 'passed'
# with tricky peak_width
expected_results = numpy.array([1.0, 1.2])
results = cet(test_data, 3, 0.20)
print 'testing %s expected to be %s' % (results, expected_results)
self.assertEqual(expected_results.shape, results.shape)
self.assertTrue(numpy.equal(expected_results, results).all())
print 'passed'
def test_min_num_channels(self):
# If min_num_channels is greater than the number of channels in
# event_times then just return the flattened list of event_times.
flattened_data = numpy.array([0.99, 1.0, 1.01, 1.19, 1.2, 1.21,
1.29, 1.3, 1.31])
results = cet(test_data, 100, 0.2)
print 'testing %s expected to be %s' % (results, flattened_data)
self.assertEqual(flattened_data.shape, results.shape)
self.assertTrue(numpy.equal(flattened_data, results).all())
print 'passed'
# same as if min_num_channels is 1
results = cet(test_data, 1, 0.001) # peak drift ignored
print 'testing %s expected to be %s' % (results, flattened_data)
self.assertEqual(flattened_data.shape, results.shape)
self.assertTrue(numpy.equal(flattened_data, results).all())
print 'passed'
results = cet(test_data, 1, 4) # peak drift ignored
print 'testing %s expected to be %s' % (results, flattened_data)
self.assertEqual(flattened_data.shape, results.shape)
self.assertTrue(numpy.equal(flattened_data, results).all())
print 'passed'