def test_calc_mse_t_weights():
with pytest.raises(ValueError):
# t_start and t_weights
MSEMetric(t_start=1 * ms, t_weights=np.ones(20))
with pytest.raises(ValueError):
# all values zero
MSEMetric(t_weights=np.zeros(20))
with pytest.raises(ValueError):
# negative values
weights = np.ones(20)
weights[17] = -1
MSEMetric(t_weights=weights)
weights = np.ones(20)
weights[:10] = 0
mse = MSEMetric(t_weights=weights)
out = np.random.rand(2, 20)
inp = np.random.rand(5, 2, 20)
errors = mse.calc(inp, out, 0.1 * ms)
assert_equal(np.shape(errors), (5, ))
assert (np.all(errors > 0))
# Everything before 1ms should be ignored, so having the same values for
# the rest should give an error of 0
inp[:, :, 10:] = out[None, :, 10:]
assert_equal(mse.calc(inp, out, 0.1 * ms), np.zeros(5))
def test_calc_mse_t_start():
mse = MSEMetric(t_start=1 * ms)
out = np.random.rand(2, 20)
inp = np.random.rand(5, 2, 20)
errors = mse.calc(inp, out, 0.1 * ms)
assert_equal(np.shape(errors), (5, ))
assert (np.all(errors > 0))
# Everything before 1ms should be ignored, so having the same values for
# the rest should give an error of 0
inp[:, :, 10:] = out[None, :, 10:]
assert_equal(mse.calc(inp, out, 0.1 * ms), np.zeros(5))
def test_calc_mse():
mse = MSEMetric()
out = np.random.rand(2, 20)
inp = np.random.rand(5, 2, 20)
errors = mse.calc(inp, out, 0.01 * ms)
assert_equal(np.shape(errors), (5, ))
assert_equal(mse.calc(np.tile(out, (5, 1, 1)), out, 0.1 * ms), np.zeros(5))
assert (np.all(mse.calc(inp, out, 0.1 * ms) > 0))
inp = np.vstack([np.ones((1, 3, 10)), np.zeros((1, 3, 10))])
out = np.ones((3, 10))
errors = mse.calc(inp, out, 0.01 * ms)
assert_equal(errors, [0, 1])
mse = MSEMetric(normalization=1 / 2)
errors = mse.calc(inp, out, 0.01 * ms)
# The normalization factor scales the traces, so the squared error scales
# with the square of the normalization factor
assert_equal(errors, [0, 4])
def test_calc_mse_t_weights_normalization():
# check that normalization works correctly
dt = 0.1 * ms
metric1 = MSEMetric(t_start=50 * dt)
weights = np.ones(100)
weights[:50] = 0
metric2 = MSEMetric(t_weights=weights)
weights2 = weights * 2 # should not make any difference
metric3 = MSEMetric(t_weights=weights2)
data_traces = np.random.rand(3, 100)
model_traces = np.random.rand(2, 3, 100)
error_1 = metric1.calc(model_traces=model_traces,
data_traces=data_traces,
dt=dt)
error_2 = metric2.calc(model_traces=model_traces,
data_traces=data_traces,
dt=dt)
error_3 = metric3.calc(model_traces=model_traces,
data_traces=data_traces,
dt=dt)
assert_almost_equal(error_1, error_2)
assert_almost_equal(error_1, error_3)