def test_correlation():
cc = channel.Slice(channel.Continuous(np.arange(10, 80, 2), 10, 2))
# Test image stack without dead time
fake_tiff = TiffStack(
MockTiff([["10", "20"], ["20", "30"], ["30", "40"], ["40", "50"],
["50", "60"], ["60", "70"]]))
stack = CorrelatedStack.from_data(fake_tiff)
assert (np.allclose(
np.hstack([cc[x.start:x.stop].data for x in stack[2:4]]),
np.arange(30, 50, 2)))
# Test image stack with dead time
fake_tiff = TiffStack(
MockTiff([["10", "18"], ["20", "28"], ["30", "38"], ["40", "48"],
["50", "58"], ["60", "68"]]))
stack = CorrelatedStack.from_data(fake_tiff)
assert (np.allclose(
np.hstack([cc[x.start:x.stop].data for x in stack[2:4]]),
np.hstack([np.arange(30, 38, 2),
np.arange(40, 48, 2)])))
# Unit test which tests whether we obtain an appropriately downsampled time series when ask for downsampling of a
# slice based on a stack.
ch = cc.downsampled_over(stack[0:3].timestamps)
assert (np.allclose(ch.data, [
np.mean(np.arange(10, 18, 2)),
np.mean(np.arange(20, 28, 2)),
np.mean(np.arange(30, 38, 2))
]))
assert (np.allclose(ch.timestamps, [(10 + 18) / 2, (20 + 28) / 2,
(30 + 38) / 2]))
ch = cc.downsampled_over(stack[1:4].timestamps)
assert (np.allclose(ch.data, [
np.mean(np.arange(20, 28, 2)),
np.mean(np.arange(30, 38, 2)),
np.mean(np.arange(40, 48, 2))
]))
assert (np.allclose(ch.timestamps, [(20 + 28) / 2, (30 + 38) / 2,
(40 + 48) / 2]))
with pytest.raises(TypeError):
cc.downsampled_over(stack[1:4])
with pytest.raises(ValueError):
cc.downsampled_over(stack[1:4].timestamps, where='up')
with pytest.raises(AssertionError):
cc["0ns":"20ns"].downsampled_over(stack[3:4].timestamps)
with pytest.raises(AssertionError):
cc["40ns":"70ns"].downsampled_over(stack[0:1].timestamps)
assert (stack[0].raw.start == 10)
assert (stack[1].raw.start == 20)
assert (stack[1:3][0].raw.start == 20)
assert (stack[1:3].raw[0].start == 20)
assert (stack[1:3].raw[1].start == 30)
def test_correlated_stack():
fake_tiff = TiffStack(
MockTiff([["10", "18"], ["20", "28"], ["30", "38"], ["40", "48"],
["50", "58"], ["60", "68"]]))
stack = CorrelatedStack.from_data(fake_tiff)
assert (stack[0].start == 10)
assert (stack[1].start == 20)
assert (stack[-1].start == 60)
assert (stack[0].num_frames == 1)
assert (stack[0].stop == 18)
assert (stack[-1].stop == 68)
assert (stack[1:2].stop == 28)
assert (stack[1:3].stop == 38)
assert (stack[1:2].num_frames == 1)
assert (stack[1:3].num_frames == 2)
assert (stack[3:5][0].start == 40)
assert (stack[3:5][1].start == 50)
assert (stack[3:5][0].num_frames == 1)
with pytest.raises(IndexError):
stack[3:5][2]
assert (stack[2:5][3:5].num_frames == 0)
assert (stack[2:5][1:2].start == 40)
assert (stack[2:5][1:3]._get_frame(1).start == 50)
with pytest.raises(IndexError):
stack[::2]
with pytest.raises(IndexError):
stack[1:2]._get_frame(1).stop
# Integration test whether slicing from the stack object actually provides you with correct slices
assert (np.allclose(stack[2:5].start, 30))
assert (np.allclose(stack[2:5].stop, 58))
# Test iterations
assert (np.allclose([x.start for x in stack], [10, 20, 30, 40, 50, 60]))
assert (np.allclose([x.start for x in stack[1:]], [20, 30, 40, 50, 60]))
assert (np.allclose([x.start for x in stack[:-1]], [10, 20, 30, 40, 50]))
assert (np.allclose([x.start for x in stack[2:4]], [30, 40]))
assert (np.allclose([x.start for x in stack[2]], [30]))
def test_name_change_from_data():
fake_tiff = TiffStack(MockTiffFile(data=[np.ones((5, 4, 3))],
times=make_frame_times(1)),
align_requested=False)
with pytest.deprecated_call():
CorrelatedStack.from_data(fake_tiff)