def test_indexed(self):
# Here we slice two of the dimensions and then compare the results to a
# manually sliced dataset.
derived = IndexedData(self.data, (None, 2, None, 4, None))
manual = Data()
manual.add_component(self.data[self.x_id][:, 2, :, 4, :], label=self.x_id)
manual.add_component(self.data[self.y_id][:, 2, :, 4, :], label=self.y_id)
assert derived.label == 'Test data[:,2,:,4,:]'
assert derived.shape == manual.shape
assert [str(c) for c in derived.main_components] == [str(c) for c in manual.main_components]
assert derived.get_kind(self.x_id) == manual.get_kind(self.x_id)
for view in [None, (1, slice(None), slice(1, 4))]:
assert_equal(derived.get_data(self.x_id, view=view),
manual.get_data(self.x_id, view=view))
assert_equal(derived.get_mask(self.subset_state, view=view),
manual.get_mask(self.subset_state, view=view))
bounds = [2, (-5, 5, 10), (-3, 3, 10)]
assert_equal(derived.compute_fixed_resolution_buffer(bounds=bounds, target_cid=self.x_id),
manual.compute_fixed_resolution_buffer(bounds=bounds, target_cid=self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id),
manual.compute_statistic('mean', self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id, axis=2),
manual.compute_statistic('mean', self.x_id, axis=2))
assert_equal(derived.compute_statistic('mean', self.x_id, subset_state=self.subset_state),
manual.compute_statistic('mean', self.x_id, subset_state=self.subset_state))
assert_equal(derived.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30]),
manual.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30]))
assert_equal(derived.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30], subset_state=self.subset_state),
manual.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30], subset_state=self.subset_state))
def test_indexed(self):
# Here we slice two of the dimensions and then compare the results to a
# manually sliced dataset.
derived = IndexedData(self.data, (None, 2, None, 4, None))
manual = Data()
manual.add_component(self.data[self.x_id][:, 2, :, 4, :],
label=self.x_id)
manual.add_component(self.data[self.y_id][:, 2, :, 4, :],
label=self.y_id)
assert derived.label == 'Test data[:,2,:,4,:]'
assert derived.shape == manual.shape
assert [str(c) for c in derived.main_components
] == [str(c) for c in manual.main_components]
assert derived.get_kind(self.x_id) == manual.get_kind(self.x_id)
for view in [None, (1, slice(None), slice(1, 4))]:
assert_equal(derived.get_data(self.x_id, view=view),
manual.get_data(self.x_id, view=view))
assert_equal(derived.get_mask(self.subset_state, view=view),
manual.get_mask(self.subset_state, view=view))
bounds = [2, (-5, 5, 10), (-3, 3, 10)]
assert_equal(
derived.compute_fixed_resolution_buffer(bounds=bounds,
target_cid=self.x_id),
manual.compute_fixed_resolution_buffer(bounds=bounds,
target_cid=self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id),
manual.compute_statistic('mean', self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id, axis=2),
manual.compute_statistic('mean', self.x_id, axis=2))
assert_equal(
derived.compute_statistic('mean',
self.x_id,
subset_state=self.subset_state),
manual.compute_statistic('mean',
self.x_id,
subset_state=self.subset_state))
assert_equal(
derived.compute_histogram([self.x_id],
range=[(0, 1000)],
bins=[30]),
manual.compute_histogram([self.x_id], range=[(0, 1000)],
bins=[30]))
assert_equal(
derived.compute_histogram([self.x_id],
range=[(0, 1000)],
bins=[30],
subset_state=self.subset_state),
manual.compute_histogram([self.x_id],
range=[(0, 1000)],
bins=[30],
subset_state=self.subset_state))
class TestIndexedData:
def setup_class(self):
x = +np.arange(2520).reshape((3, 4, 5, 6, 7))
y = -np.arange(2520).reshape((3, 4, 5, 6, 7))
self.data = Data(x=x, y=y, label='Test data')
self.x_id, self.y_id = self.data.main_components
matrix = np.random.random((6, 6)) - 0.5
matrix[-1] = [0, 0, 0, 0, 0, 1]
self.data_with_coords = Data(x=x,
y=y,
label='Test data',
coords=AffineCoordinates(matrix=matrix))
self.subset_state = self.x_id >= 1200
def test_identity(self):
# In this test, we don't actually slice any dimensions
derived = IndexedData(self.data, (None, ) * 5)
assert derived.label == 'Test data[:,:,:,:,:]'
assert derived.shape == self.data.shape
assert [str(c) for c in derived.main_components
] == [str(c) for c in self.data.main_components]
assert derived.get_kind(self.x_id) == self.data.get_kind(self.x_id)
for view in [
None, (1, slice(None), slice(None), slice(1,
4), slice(0, 7, 2))
]:
assert_equal(derived.get_data(self.x_id, view=view),
self.data.get_data(self.x_id, view=view))
assert_equal(derived.get_mask(self.subset_state, view=view),
self.data.get_mask(self.subset_state, view=view))
bounds = [2, (-5, 5, 10), 3, 4, (-3, 3, 10)]
assert_equal(
derived.compute_fixed_resolution_buffer(bounds=bounds,
target_cid=self.x_id),
self.data.compute_fixed_resolution_buffer(bounds=bounds,
target_cid=self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id),
self.data.compute_statistic('mean', self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id, axis=2),
self.data.compute_statistic('mean', self.x_id, axis=2))
assert_equal(
derived.compute_statistic('mean',
self.x_id,
subset_state=self.subset_state),
self.data.compute_statistic('mean',
self.x_id,
subset_state=self.subset_state))
assert_equal(
derived.compute_histogram([self.x_id],
range=[(0, 1000)],
bins=[30]),
self.data.compute_histogram([self.x_id],
range=[(0, 1000)],
bins=[30]))
assert_equal(
derived.compute_histogram([self.x_id],
range=[(0, 1000)],
bins=[30],
subset_state=self.subset_state),
self.data.compute_histogram([self.x_id],
range=[(0, 1000)],
bins=[30],
subset_state=self.subset_state))
def test_indexed(self):
# Here we slice two of the dimensions and then compare the results to a
# manually sliced dataset.
derived = IndexedData(self.data, (None, 2, None, 4, None))
manual = Data()
manual.add_component(self.data[self.x_id][:, 2, :, 4, :],
label=self.x_id)
manual.add_component(self.data[self.y_id][:, 2, :, 4, :],
label=self.y_id)
assert derived.label == 'Test data[:,2,:,4,:]'
assert derived.shape == manual.shape
assert [str(c) for c in derived.main_components
] == [str(c) for c in manual.main_components]
assert derived.get_kind(self.x_id) == manual.get_kind(self.x_id)
for view in [None, (1, slice(None), slice(1, 4))]:
assert_equal(derived.get_data(self.x_id, view=view),
manual.get_data(self.x_id, view=view))
assert_equal(derived.get_mask(self.subset_state, view=view),
manual.get_mask(self.subset_state, view=view))
bounds = [2, (-5, 5, 10), (-3, 3, 10)]
assert_equal(
derived.compute_fixed_resolution_buffer(bounds=bounds,
target_cid=self.x_id),
manual.compute_fixed_resolution_buffer(bounds=bounds,
target_cid=self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id),
manual.compute_statistic('mean', self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id, axis=2),
manual.compute_statistic('mean', self.x_id, axis=2))
assert_equal(
derived.compute_statistic('mean',
self.x_id,
subset_state=self.subset_state),
manual.compute_statistic('mean',
self.x_id,
subset_state=self.subset_state))
assert_equal(
derived.compute_histogram([self.x_id],
range=[(0, 1000)],
bins=[30]),
manual.compute_histogram([self.x_id], range=[(0, 1000)],
bins=[30]))
assert_equal(
derived.compute_histogram([self.x_id],
range=[(0, 1000)],
bins=[30],
subset_state=self.subset_state),
manual.compute_histogram([self.x_id],
range=[(0, 1000)],
bins=[30],
subset_state=self.subset_state))
def test_numerical_values_changed(self):
# Here we slice two of the dimensions and then compare the results to a
# manually sliced dataset.
derived = IndexedData(self.data, (None, 2, None, 4, None))
data_collection = DataCollection([self.data, derived])
class CustomListener(HubListener):
def __init__(self, hub):
self.received = 0
hub.subscribe(self,
NumericalDataChangedMessage,
handler=self.receive_message)
def receive_message(self, message):
self.received += 1
listener = CustomListener(data_collection.hub)
assert listener.received == 0
derived.indices = (None, 3, None, 5, None)
assert listener.received == 1
derived.indices = (None, 3, None, 5, None)
assert listener.received == 1
def test_invalid_indices_init(self):
with pytest.raises(ValueError) as exc:
derived = IndexedData(self.data, (2, None, 4, None))
assert exc.value.args[0] == "The 'indices' tuple should have length 5"
def test_invalid_indices_changed(self):
derived = IndexedData(self.data, (None, 2, None, 4, None))
with pytest.raises(TypeError) as exc:
derived.indices = (2, None, 4, None, 5)
assert exc.value.args[
0] == "Can't change where the ``None`` values are in indices"
with pytest.raises(ValueError) as exc:
derived.indices = (2, None, 4, None)
assert exc.value.args[0] == "The 'indices' tuple should have length 5"
def test_pixel_component_ids(self):
derived = IndexedData(self.data, (None, 2, None, 4, None))
assert_equal(
derived.get_data(derived.pixel_component_ids[1]),
self.data.get_data(self.data.pixel_component_ids[2])[:, 2, :,
4, :])
def test_world_component_ids(self):
derived = IndexedData(self.data, (None, 2, None, 4, None))
assert derived.world_component_ids == []
derived_with_coords = IndexedData(self.data_with_coords,
(None, 2, None, 4, None))
assert_equal(
derived_with_coords.get_data(
derived_with_coords.world_component_ids[0]),
self.data_with_coords.get_data(
self.data_with_coords.world_component_ids[0])[:, 2, :, 4, :])
assert_equal(
derived_with_coords.get_data(
derived_with_coords.world_component_ids[1]),
self.data_with_coords.get_data(
self.data_with_coords.world_component_ids[2])[:, 2, :, 4, :])
assert_equal(
derived_with_coords.get_data(
derived_with_coords.world_component_ids[2]),
self.data_with_coords.get_data(
self.data_with_coords.world_component_ids[4])[:, 2, :, 4, :])
class TestIndexedData:
def setup_class(self):
x = +np.arange(2520).reshape((3, 4, 5, 6, 7))
y = -np.arange(2520).reshape((3, 4, 5, 6, 7))
self.data = Data(x=x, y=y, label='Test data')
self.x_id, self.y_id = self.data.main_components
self.subset_state = self.x_id >= 1200
def test_identity(self):
# In this test, we don't actually slice any dimensions
derived = IndexedData(self.data, (None,) * 5)
assert derived.label == 'Test data[:,:,:,:,:]'
assert derived.shape == self.data.shape
assert [str(c) for c in derived.main_components] == [str(c) for c in self.data.main_components]
assert derived.get_kind(self.x_id) == self.data.get_kind(self.x_id)
for view in [None, (1, slice(None), slice(None), slice(1, 4), slice(0, 7, 2))]:
assert_equal(derived.get_data(self.x_id, view=view),
self.data.get_data(self.x_id, view=view))
assert_equal(derived.get_mask(self.subset_state, view=view),
self.data.get_mask(self.subset_state, view=view))
bounds = [2, (-5, 5, 10), 3, 4, (-3, 3, 10)]
assert_equal(derived.compute_fixed_resolution_buffer(bounds=bounds, target_cid=self.x_id),
self.data.compute_fixed_resolution_buffer(bounds=bounds, target_cid=self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id),
self.data.compute_statistic('mean', self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id, axis=2),
self.data.compute_statistic('mean', self.x_id, axis=2))
assert_equal(derived.compute_statistic('mean', self.x_id, subset_state=self.subset_state),
self.data.compute_statistic('mean', self.x_id, subset_state=self.subset_state))
assert_equal(derived.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30]),
self.data.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30]))
assert_equal(derived.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30], subset_state=self.subset_state),
self.data.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30], subset_state=self.subset_state))
def test_indexed(self):
# Here we slice two of the dimensions and then compare the results to a
# manually sliced dataset.
derived = IndexedData(self.data, (None, 2, None, 4, None))
manual = Data()
manual.add_component(self.data[self.x_id][:, 2, :, 4, :], label=self.x_id)
manual.add_component(self.data[self.y_id][:, 2, :, 4, :], label=self.y_id)
assert derived.label == 'Test data[:,2,:,4,:]'
assert derived.shape == manual.shape
assert [str(c) for c in derived.main_components] == [str(c) for c in manual.main_components]
assert derived.get_kind(self.x_id) == manual.get_kind(self.x_id)
for view in [None, (1, slice(None), slice(1, 4))]:
assert_equal(derived.get_data(self.x_id, view=view),
manual.get_data(self.x_id, view=view))
assert_equal(derived.get_mask(self.subset_state, view=view),
manual.get_mask(self.subset_state, view=view))
bounds = [2, (-5, 5, 10), (-3, 3, 10)]
assert_equal(derived.compute_fixed_resolution_buffer(bounds=bounds, target_cid=self.x_id),
manual.compute_fixed_resolution_buffer(bounds=bounds, target_cid=self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id),
manual.compute_statistic('mean', self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id, axis=2),
manual.compute_statistic('mean', self.x_id, axis=2))
assert_equal(derived.compute_statistic('mean', self.x_id, subset_state=self.subset_state),
manual.compute_statistic('mean', self.x_id, subset_state=self.subset_state))
assert_equal(derived.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30]),
manual.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30]))
assert_equal(derived.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30], subset_state=self.subset_state),
manual.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30], subset_state=self.subset_state))
def test_numerical_values_changed(self):
# Here we slice two of the dimensions and then compare the results to a
# manually sliced dataset.
derived = IndexedData(self.data, (None, 2, None, 4, None))
data_collection = DataCollection([self.data, derived])
class CustomListener(HubListener):
def __init__(self, hub):
self.received = 0
hub.subscribe(self, NumericalDataChangedMessage,
handler=self.receive_message)
def receive_message(self, message):
self.received += 1
listener = CustomListener(data_collection.hub)
assert listener.received == 0
derived.indices = (None, 3, None, 5, None)
assert listener.received == 1
derived.indices = (None, 3, None, 5, None)
assert listener.received == 1
def test_invalid_indices_init(self):
with pytest.raises(ValueError) as exc:
derived = IndexedData(self.data, (2, None, 4, None))
assert exc.value.args[0] == "The 'indices' tuple should have length 5"
def test_invalid_indices_changed(self):
derived = IndexedData(self.data, (None, 2, None, 4, None))
with pytest.raises(TypeError) as exc:
derived.indices = (2, None, 4, None, 5)
assert exc.value.args[0] == "Can't change where the ``None`` values are in indices"
with pytest.raises(ValueError) as exc:
derived.indices = (2, None, 4, None)
assert exc.value.args[0] == "The 'indices' tuple should have length 5"
def test_pixel_component_ids(self):
derived = IndexedData(self.data, (None, 2, None, 4, None))
assert_equal(derived.get_data(derived.pixel_component_ids[1]),
self.data.get_data(self.data.pixel_component_ids[2])[:, 2, :, 4, :])
