def test_dual_mean_of_difference(self):
# MEAN(B - A) and MEAN(C - A)
shape = (500, 30, 40)
size = np.prod(shape)
raw_data = np.linspace(0, 1, num=size).reshape(shape)
a_counter = AccessCounter(raw_data)
a_array = biggus.NumpyArrayAdapter(a_counter)
b_counter = AccessCounter(raw_data * 3)
b_array = biggus.NumpyArrayAdapter(b_counter)
c_counter = AccessCounter(raw_data * 5)
c_array = biggus.NumpyArrayAdapter(c_counter)
b_sub_a_array = biggus.sub(b_array, a_array)
mean_b_sub_a_array = biggus.mean(b_sub_a_array, axis=0)
c_sub_a_array = biggus.sub(c_array, a_array)
mean_c_sub_a_array = biggus.mean(c_sub_a_array, axis=0)
mean_b_sub_a, mean_c_sub_a = biggus.ndarrays(
[mean_b_sub_a_array, mean_c_sub_a_array])
# Are the resulting numbers equivalent?
np.testing.assert_array_almost_equal(mean_b_sub_a,
np.mean(raw_data * 2, axis=0))
np.testing.assert_array_almost_equal(mean_c_sub_a,
np.mean(raw_data * 4, axis=0))
# Was the source data read just once?
self.assert_counts(a_counter.counts, [1])
self.assert_counts(b_counter.counts, [1])
self.assert_counts(c_counter.counts, [1])
def test_dual_mean_of_difference(self):
# MEAN(B - A) and MEAN(C - A)
shape = (500, 30, 40)
size = np.prod(shape)
raw_data = np.linspace(0, 1, num=size).reshape(shape)
a_counter = AccessCounter(raw_data)
a_array = biggus.NumpyArrayAdapter(a_counter)
b_counter = AccessCounter(raw_data * 3)
b_array = biggus.NumpyArrayAdapter(b_counter)
c_counter = AccessCounter(raw_data * 5)
c_array = biggus.NumpyArrayAdapter(c_counter)
b_sub_a_array = biggus.sub(b_array, a_array)
mean_b_sub_a_array = biggus.mean(b_sub_a_array, axis=0)
c_sub_a_array = biggus.sub(c_array, a_array)
mean_c_sub_a_array = biggus.mean(c_sub_a_array, axis=0)
mean_b_sub_a, mean_c_sub_a = biggus.ndarrays([mean_b_sub_a_array,
mean_c_sub_a_array])
# Are the resulting numbers equivalent?
np.testing.assert_array_almost_equal(mean_b_sub_a,
np.mean(raw_data * 2, axis=0))
np.testing.assert_array_almost_equal(mean_c_sub_a,
np.mean(raw_data * 4, axis=0))
# Was the source data read just once?
self.assert_counts(a_counter.counts, [1])
self.assert_counts(b_counter.counts, [1])
self.assert_counts(c_counter.counts, [1])
def test_mean_of_a_and_mean_of_difference(self):
# MEAN(A) and MEAN(A - B)
shape = (500, 30, 40)
size = np.prod(shape)
raw_data = np.linspace(0, 1, num=size).reshape(shape)
a_counter = AccessCounter(raw_data * 3)
a_array = biggus.NumpyArrayAdapter(a_counter)
b_counter = AccessCounter(raw_data)
b_array = biggus.NumpyArrayAdapter(b_counter)
sub_array = biggus.sub(a_array, b_array)
mean_a_array = biggus.mean(a_array, axis=0)
mean_sub_array = biggus.mean(sub_array, axis=0)
mean_a, mean_sub = biggus.ndarrays([mean_a_array, mean_sub_array])
# Are the resulting numbers equivalent?
np.testing.assert_array_almost_equal(mean_a,
np.mean(raw_data * 3, axis=0))
np.testing.assert_array_almost_equal(mean_sub,
np.mean(raw_data * 2, axis=0))
# Was the source data read the minimal number of times?
# (Allow first slice of A to be read twice because both `mean`
# operations use it to to bootstrap their calculations.)
self.assert_counts(a_counter.counts[0], [2])
self.assert_counts(a_counter.counts[1:], [1])
self.assert_counts(b_counter.counts, [1])
def test_mean_of_difference(self):
shape = (3, 4)
size = np.prod(shape)
raw_data1 = np.linspace(0.2, 1.0, num=size).reshape(shape)
raw_data2 = np.linspace(0.3, 1.5, num=size).reshape(shape)
array1 = biggus.NumpyArrayAdapter(raw_data1)
array2 = biggus.NumpyArrayAdapter(raw_data2)
difference = biggus.sub(array2, array1)
mean_difference = biggus.mean(difference, axis=0)
# Check the NumPy and biggus numeric values match.
result = mean_difference.ndarray()
numpy_result = np.mean(raw_data2 - raw_data1, axis=0)
np.testing.assert_array_equal(result, numpy_result)
def test_mean_of_difference(self):
shape = (3, 4)
size = np.prod(shape)
raw_data1 = np.linspace(0.2, 1.0, num=size).reshape(shape)
raw_data2 = np.linspace(0.3, 1.5, num=size).reshape(shape)
array1 = biggus.NumpyArrayAdapter(raw_data1)
array2 = biggus.NumpyArrayAdapter(raw_data2)
difference = biggus.sub(array2, array1)
mean_difference = biggus.mean(difference, axis=0)
# Check the NumPy and biggus numeric values match.
result = mean_difference.ndarray()
numpy_result = np.mean(raw_data2 - raw_data1, axis=0)
np.testing.assert_array_equal(result, numpy_result)
def test_mean_of_difference(self):
# MEAN(A - B)
shape = (500, 30, 40)
size = np.prod(shape)
raw_data = np.linspace(0, 1, num=size).reshape(shape)
data = AccessCounter(raw_data * 3)
a_array = biggus.NumpyArrayAdapter(data)
data = AccessCounter(raw_data)
b_array = biggus.NumpyArrayAdapter(data)
mean_array = biggus.mean(biggus.sub(a_array, b_array), axis=0)
mean = mean_array.ndarray()
np.testing.assert_array_almost_equal(mean,
np.mean(raw_data * 2, axis=0))
def test_mean_of_difference(self):
# MEAN(A - B)
shape = (500, 30, 40)
size = np.prod(shape)
raw_data = np.linspace(0, 1, num=size).reshape(shape)
data = AccessCounter(raw_data * 3)
a_array = biggus.NumpyArrayAdapter(data)
data = AccessCounter(raw_data)
b_array = biggus.NumpyArrayAdapter(data)
mean_array = biggus.mean(biggus.sub(a_array, b_array), axis=0)
mean = mean_array.ndarray()
np.testing.assert_array_almost_equal(mean,
np.mean(raw_data * 2, axis=0))
def test_sd_and_mean_of_difference(self):
# MEAN(A - B) and SD(A - B)
shape = (500, 30, 40)
size = np.prod(shape)
raw_data = np.linspace(0, 1, num=size).reshape(shape)
a_counter = AccessCounter(raw_data * 3)
a_array = biggus.NumpyArrayAdapter(a_counter)
b_counter = AccessCounter(raw_data)
b_array = biggus.NumpyArrayAdapter(b_counter)
sub_array = biggus.sub(a_array, b_array)
mean_array = biggus.mean(sub_array, axis=0)
std_array = biggus.std(sub_array, axis=0)
mean, std = biggus.ndarrays([mean_array, std_array])
# Are the resulting numbers equivalent?
np.testing.assert_array_almost_equal(mean, np.mean(raw_data * 2,
axis=0))
np.testing.assert_array_almost_equal(std, np.std(raw_data * 2, axis=0))
# Was the source data read just once?
self.assert_counts(a_counter.counts, [1])
self.assert_counts(b_counter.counts, [1])
def test_sd_and_mean_of_difference(self):
# MEAN(A - B) and SD(A - B)
shape = (500, 30, 40)
size = np.prod(shape)
raw_data = np.linspace(0, 1, num=size).reshape(shape)
a_counter = AccessCounter(raw_data * 3)
a_array = biggus.NumpyArrayAdapter(a_counter)
b_counter = AccessCounter(raw_data)
b_array = biggus.NumpyArrayAdapter(b_counter)
sub_array = biggus.sub(a_array, b_array)
mean_array = biggus.mean(sub_array, axis=0)
std_array = biggus.std(sub_array, axis=0)
mean, std = biggus.ndarrays([mean_array, std_array])
# Are the resulting numbers equivalent?
np.testing.assert_array_almost_equal(mean,
np.mean(raw_data * 2, axis=0))
np.testing.assert_array_almost_equal(std,
np.std(raw_data * 2, axis=0))
# Was the source data read just once?
self.assert_counts(a_counter.counts, [1])
self.assert_counts(b_counter.counts, [1])
def test_other_array(self):
a = FakeArray([2, 4])
b = FakeArray([2, 4])
r = a - b
self.assertIsInstance(r, biggus._Elementwise)
self.assertElementwise(r, biggus.sub(a, b))
def test_other_array(self):
a = FakeArray([2, 4])
b = FakeArray([2, 4])
r = a - b
self.assertIsInstance(r, biggus._Elementwise)
self.assertElementwise(r, biggus.sub(a, b))