def test_image_reconstruction_rgb_missing_metadata(rgb_alignment_image_data):
# no metadata
reference_image, warped_image, description, bit_depth = rgb_alignment_image_data
with pytest.warns(
UserWarning, match="File does not contain metadata. Only raw data is available"
):
fake_tiff = TiffStack(
MockTiffFile(
data=[warped_image], times=make_frame_times(1), description="", bit_depth=16
),
align_requested=True,
)
# missing alignment matrices
for label in ("Alignment red channel", "Channel 0 alignment"):
if label in description:
removed = description.pop(label)
break
with pytest.warns(
UserWarning, match="File does not contain alignment matrices. Only raw data is available"
):
fake_tiff = TiffStack(
MockTiffFile(
data=[warped_image],
times=make_frame_times(1),
description=json.dumps(description),
bit_depth=16,
),
align_requested=True,
)
description[label] = removed # reset fixture
def test_plotting(rgb_alignment_image_data):
reference_image, warped_image, description, bit_depth = rgb_alignment_image_data
fake_tiff = TiffStack(
MockTiffFile(
data=[warped_image] * 2,
times=make_frame_times(2),
description=json.dumps(description),
bit_depth=16,
),
align_requested=True,
)
stack = CorrelatedStack.from_dataset(fake_tiff)
stack.plot(channel="blue", frame=0)
ref_image = stack._get_frame(0)._get_plot_data(channel="blue")
image = plt.gca().get_images()[0]
np.testing.assert_allclose(image.get_array(), ref_image)
plt.close()
stack.plot(channel="rgb", frame=0)
ref_image = stack._get_frame(0)._get_plot_data(channel="rgb")
image = plt.gca().get_images()[0]
np.testing.assert_allclose(image.get_array(), ref_image)
plt.close()
with pytest.raises(IndexError, match="Frame index out of range"):
stack.plot(channel="blue", frame=4)
with pytest.raises(IndexError, match="Frame index out of range"):
stack.plot(channel="blue", frame=-1)
def make_mock_stack():
spot_coordinates = ((25, 25), (50, 50))
warp_parameters = {
"red_warp_parameters": {
"Tx": 0,
"Ty": 0,
"theta": 0
},
"blue_warp_parameters": {
"Tx": 0,
"Ty": 0,
"theta": 0
},
}
_, image, description, bit_depth = make_alignment_image_data(
spot_coordinates,
version=2,
bit_depth=16,
camera="wt",
**warp_parameters)
tiff = TiffStack(
MockTiffFile(
data=[image, image, image],
times=[["10", "18"], ["20", "28"], ["30", "38"]],
description=json.dumps(description),
bit_depth=bit_depth,
),
align_requested=True,
)
return CorrelatedStack.from_dataset(tiff)
def test_deprecate_raw():
fake_tiff = TiffStack(MockTiffFile(data=[np.ones((5, 4, 3))],
times=make_frame_times(1)),
align_requested=False)
stack = CorrelatedStack.from_dataset(fake_tiff)
with pytest.deprecated_call():
stack.raw
def make_stack(data, description, bit_depth):
tiff = TiffStack(
MockTiffFile(
data=[data],
times=make_frame_times(1),
description=json.dumps(description),
bit_depth=bit_depth,
),
align_requested=True,
)
return CorrelatedStack.from_dataset(tiff)
def test_get_image():
# grayscale image - multiple frames
data = [np.full((2, 2), j) for j in range(3)]
times = make_frame_times(3)
fake_tiff = TiffStack(MockTiffFile(data, times), align_requested=False)
stack = CorrelatedStack.from_dataset(fake_tiff)
np.testing.assert_array_equal(np.stack(data, axis=0), stack.get_image())
# grayscale image - single frame
fake_tiff = TiffStack(MockTiffFile([data[0]], [times[0]]),
align_requested=False)
stack = CorrelatedStack.from_dataset(fake_tiff)
np.testing.assert_array_equal(data[0], stack.get_image())
# RGB image - multiple frames
rgb_data = np.stack([np.full((2, 2), j) for j in range(3)], axis=2)
data = [rgb_data] * 3
fake_tiff = TiffStack(MockTiffFile(data, times), align_requested=False)
stack = CorrelatedStack.from_dataset(fake_tiff)
for j, color in enumerate(("red", "green", "blue")):
np.testing.assert_array_equal(stack.get_image(channel=color),
j,
err_msg=f"failed on {color}")
np.testing.assert_array_equal(np.stack(data, axis=0), stack.get_image())
np.testing.assert_array_equal(np.stack(data, axis=0),
stack.get_image(channel="rgb"))
# RGB image - multiple frames
fake_tiff = TiffStack(MockTiffFile([data[0]], [times[0]]),
align_requested=False)
stack = CorrelatedStack.from_dataset(fake_tiff)
for j, color in enumerate(("red", "green", "blue")):
np.testing.assert_array_equal(stack.get_image(channel=color),
data[0][:, :, j],
err_msg=f"failed on {color}")
np.testing.assert_array_equal(data[0], stack.get_image())
np.testing.assert_array_equal(data[0], stack.get_image("rgb"))
def test_no_alignment_requested(rgb_alignment_image_data):
reference_image, warped_image, description, bit_depth = rgb_alignment_image_data
fake_tiff = TiffStack(
MockTiffFile(
data=[warped_image],
times=make_frame_times(1),
description=json.dumps(description),
bit_depth=16,
),
align_requested=False,
)
stack = CorrelatedStack.from_dataset(fake_tiff)
fr = stack._get_frame(0)
np.testing.assert_allclose(fr.data, warped_image)
def test_stack_roi():
first_page = np.arange(60).reshape((6, 10))
data = np.stack([first_page + (j * 60) for j in range(3)], axis=2)
stack_0 = TiffStack(MockTiffFile([data], times=make_frame_times(1)),
align_requested=False)
# recursive cropping
stack_1 = stack_0.with_roi([1, 7, 3, 6])
np.testing.assert_equal(stack_1.get_frame(0).data, data[3:6, 1:7, :])
stack_2 = stack_1.with_roi([3, 6, 0, 3])
np.testing.assert_equal(stack_2.get_frame(0).data, data[3:6, 4:7, :])
stack_3 = stack_2.with_roi([1, 2, 1, 2])
np.testing.assert_equal(stack_3.get_frame(0).data, data[4:5, 5:6, :])
# negative indices
with pytest.raises(ValueError):
stack_4 = stack_0.with_roi([-5, 4, 1, 2])
# out of bounds
with pytest.raises(ValueError):
stack_5 = stack_0.with_roi([0, 11, 1, 2])
def test_correlated_stack(shape):
fake_tiff = TiffStack(MockTiffFile(data=[np.ones(shape)] * 6,
times=make_frame_times(6)),
align_requested=False)
stack = CorrelatedStack.from_dataset(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][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
np.testing.assert_allclose(stack[2:5].start, 30)
np.testing.assert_allclose(stack[2:5].stop, 58)
# Test iterations
np.testing.assert_allclose([x.start for x in stack],
[10, 20, 30, 40, 50, 60])
np.testing.assert_allclose([x.start for x in stack[1:]],
[20, 30, 40, 50, 60])
np.testing.assert_allclose([x.start for x in stack[:-1]],
[10, 20, 30, 40, 50])
np.testing.assert_allclose([x.start for x in stack[2:4]], [30, 40])
np.testing.assert_allclose([x.start for x in stack[2]], [30])
def test_slicing(shape):
image = [np.random.poisson(10, size=shape) for _ in range(10)]
times = make_frame_times(10)
fake_tiff = TiffStack(MockTiffFile(data=image, times=times),
align_requested=False)
stack0 = CorrelatedStack.from_dataset(fake_tiff)
def compare_frames(original_frames, new_stack):
assert new_stack.num_frames == len(original_frames)
for new_frame_index, index in enumerate(original_frames):
frame = stack0._get_frame(index).data
new_frame = new_stack._get_frame(new_frame_index).data
np.testing.assert_equal(frame, new_frame)
compare_frames([0], stack0[0]) # first frame
compare_frames([9], stack0[-1]) # last frame
compare_frames([3], stack0[3]) # single frame
compare_frames([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], stack0[:]) # all frames
compare_frames([3, 4, 5], stack0[3:6]) # normal slice
compare_frames([0, 1, 2], stack0[:3]) # from beginning
compare_frames([6, 7, 8, 9], stack0[6:]) # until end
compare_frames([0, 1, 2, 3, 4, 5], stack0[:-4]) # until negative index
compare_frames([5, 6, 7], stack0[5:-2]) # mixed sign indices
compare_frames([6, 7], stack0[-4:-2]) # negative indices slice
compare_frames([3, 4], stack0[2:6][1:3]) # iterative slicing
compare_frames([1, 2, 3, 4, 5, 6, 7, 8, 9],
stack0[1:100]) # test clamping past the end
compare_frames([0, 1, 2, 3, 4, 5, 6, 7, 8],
stack0[-100:9]) # test clamping past the beginning
compare_frames([0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
stack0[-100:100]) # test clamping both dirs
# reverse slice
with pytest.raises(NotImplementedError,
match="Reverse slicing is not supported"):
stack0[5:2]
# reverse slice, negative indices
with pytest.raises(NotImplementedError,
match="Reverse slicing is not supported"):
stack0[-1:-3]
# empty slice
with pytest.raises(NotImplementedError, match="Slice is empty"):
stack0[5:5]
def test_image_reconstruction_grayscale(gray_alignment_image_data):
reference_image, warped_image, description, bit_depth = gray_alignment_image_data
fake_tiff = TiffStack(
MockTiffFile(
data=[warped_image],
times=make_frame_times(1),
description=json.dumps(description),
bit_depth=8,
),
align_requested=True,
)
stack = CorrelatedStack.from_dataset(fake_tiff)
fr = stack._get_frame(0)
assert not fr.is_rgb
assert np.all(fr.data == fr.raw_data)
np.testing.assert_allclose(fr.raw_data, fr._get_plot_data())
def test_plot_correlated_smaller_channel():
from matplotlib.backend_bases import MouseEvent
# Regression test for a bug where the start index was added twice. In the regression, this lead to an out of range
# error.
fake_tiff = TiffStack(
MockTiffFile(
data=[
np.zeros((3, 3)),
np.ones((3, 3)),
np.ones((3, 3)) * 2,
np.ones((3, 3)) * 3,
np.ones((3, 3)) * 4,
np.ones((3, 3)) * 5,
],
times=make_frame_times(7, step=10),
),
align_requested=False,
)
# Add test for when there's only a subset in terms of channel data
cc = channel.Slice(channel.Continuous(np.arange(10, 80, 2), 30, 2), {
"y": "mock",
"title": "mock"
})
with pytest.warns(UserWarning):
CorrelatedStack.from_dataset(fake_tiff).plot_correlated(cc)
def mock_click(fig, data_position):
pos = fig.axes[0].transData.transform(data_position)
fig.canvas.callbacks.process(
"button_press_event",
MouseEvent("button_press_event", fig.canvas, pos[0], pos[1], 1))
images = [
obj for obj in mpl.pyplot.gca().get_children()
if isinstance(obj, mpl.image.AxesImage)
]
assert len(images) == 1
return images[0].get_array()
np.testing.assert_allclose(mock_click(mpl.pyplot.gcf(), np.array([0, 40])),
np.ones((3, 3)) * 2)
np.testing.assert_allclose(
mock_click(mpl.pyplot.gcf(), np.array([10.1e-9, 40])),
np.ones((3, 3)) * 3)
def test_image_reconstruction_rgb_multiframe(rgb_alignment_image_data):
reference_image, warped_image, description, bit_depth = rgb_alignment_image_data
fake_tiff = TiffStack(
MockTiffFile(
data=[warped_image] * 6,
times=make_frame_times(6, step=10),
description=json.dumps(description),
bit_depth=16,
),
align_requested=True,
)
stack = CorrelatedStack.from_dataset(fake_tiff)
fr = stack._get_frame(2)
assert fr.is_rgb
original_data = (reference_image / (2 ** bit_depth - 1)).astype(float)
np.testing.assert_allclose(original_data, fr._get_plot_data(), atol=0.05)
def test_plot_correlated():
cc = channel.Slice(channel.Continuous(np.arange(10, 80, 2), 10, 2), {
"y": "mock",
"title": "mock"
})
# Regression test for a bug where the start index was added twice. In the regression, this lead to an out of range
# error.
fake_tiff = TiffStack(
MockTiffFile(
data=[
np.zeros((3, 3)),
np.ones((3, 3)),
np.ones((3, 3)) * 2,
np.ones((3, 3)) * 3,
np.ones((3, 3)) * 4,
np.ones((3, 3)) * 5,
],
times=make_frame_times(7, step=10),
),
align_requested=False,
)
CorrelatedStack.from_dataset(fake_tiff)[3:5].plot_correlated(cc)
imgs = [
obj for obj in mpl.pyplot.gca().get_children()
if isinstance(obj, mpl.image.AxesImage)
]
assert len(imgs) == 1
np.testing.assert_allclose(imgs[0].get_array(), np.ones((3, 3)) * 3)
CorrelatedStack.from_dataset(fake_tiff)[3:5].plot_correlated(cc, frame=1)
imgs = [
obj for obj in mpl.pyplot.gca().get_children()
if isinstance(obj, mpl.image.AxesImage)
]
assert len(imgs) == 1
np.testing.assert_allclose(imgs[0].get_array(), np.ones((3, 3)) * 4)
def test_image_reconstruction_rgb(rgb_alignment_image_data, rgb_alignment_image_data_offset):
reference_image, warped_image, description, bit_depth = rgb_alignment_image_data
fake_tiff = TiffStack(
MockTiffFile(
data=[warped_image],
times=make_frame_times(1),
description=json.dumps(description),
bit_depth=16,
),
align_requested=True,
)
stack = CorrelatedStack.from_dataset(fake_tiff)
fr = stack._get_frame(0)
assert fr.is_rgb
max_signal = np.max(np.hstack([fr._get_plot_data("green"), fr._get_plot_data("red")]))
diff = np.abs(fr._get_plot_data("green").astype(float) - fr._get_plot_data("red").astype(float))
assert np.all(diff / max_signal < 0.05)
max_signal = np.max(np.hstack([fr._get_plot_data("green"), fr._get_plot_data("blue")]))
diff = np.abs(
fr._get_plot_data("green").astype(float) - fr._get_plot_data("blue").astype(float)
)
assert np.all(diff / max_signal < 0.05)
original_data = (reference_image / (2 ** bit_depth - 1)).astype(float)
np.testing.assert_allclose(original_data, fr._get_plot_data(), atol=0.05)
np.testing.assert_allclose(original_data / 0.5, fr._get_plot_data(vmax=0.5), atol=0.10)
max_signal = np.max(np.hstack([reference_image[:, :, 0], fr._get_plot_data("red")]))
diff = np.abs(reference_image[:, :, 0].astype(float) - fr._get_plot_data("red").astype(float))
assert np.all(diff / max_signal < 0.05)
with pytest.raises(ValueError):
fr._get_plot_data(channel="purple")
# test that bad alignment matrix gives high error compared to correct matrix
bad_description = deepcopy(description)
label = (
"Alignment red channel"
if "Alignment red channel" in description.keys()
else "Channel 0 alignment"
)
bad_description[label][2] = 25
fake_tiff = TiffStack(
MockTiffFile(
data=[warped_image],
times=make_frame_times(1),
description=json.dumps(bad_description),
bit_depth=16,
),
align_requested=True,
)
stack = CorrelatedStack.from_dataset(fake_tiff)
fr = stack._get_frame(0)
assert fr.is_rgb
assert not np.allclose(original_data, fr._get_plot_data(), atol=0.05)
reference_image, warped_image, description, bit_depth = rgb_alignment_image_data_offset
fake_tiff = TiffStack(
MockTiffFile(
data=[warped_image],
times=make_frame_times(1),
description=json.dumps(description),
bit_depth=16,
),
align_requested=True,
)
stack = CorrelatedStack.from_dataset(fake_tiff)
fr = stack._get_frame(0)
original_data = (reference_image / (2 ** bit_depth - 1)).astype(float)
np.testing.assert_allclose(original_data, fr._get_plot_data(), atol=0.05)
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)
def test_correlation(shape):
cc = channel.Slice(channel.Continuous(np.arange(10, 80, 2), 10, 2))
# Test image stack without dead time
fake_tiff = TiffStack(
MockTiffFile(data=[np.ones(shape)] * 6,
times=make_frame_times(6, step=10)),
align_requested=False,
)
stack = CorrelatedStack.from_dataset(fake_tiff)
np.testing.assert_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(MockTiffFile(data=[np.ones(shape)] * 6,
times=make_frame_times(6)),
align_requested=False)
stack = CorrelatedStack.from_dataset(fake_tiff)
np.testing.assert_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].frame_timestamp_ranges)
np.testing.assert_allclose(
ch.data,
[
np.mean(np.arange(10, 18, 2)),
np.mean(np.arange(20, 28, 2)),
np.mean(np.arange(30, 38, 2)),
],
)
np.testing.assert_allclose(ch.timestamps, [(10 + 16) / 2, (20 + 26) / 2,
(30 + 36) / 2])
ch = cc.downsampled_over(stack[1:4].frame_timestamp_ranges)
np.testing.assert_allclose(
ch.data,
[
np.mean(np.arange(20, 28, 2)),
np.mean(np.arange(30, 38, 2)),
np.mean(np.arange(40, 48, 2)),
],
)
np.testing.assert_allclose(ch.timestamps, [(20 + 26) / 2, (30 + 36) / 2,
(40 + 46) / 2])
with pytest.raises(TypeError):
cc.downsampled_over(stack[1:4])
with pytest.raises(ValueError):
cc.downsampled_over(stack[1:4].frame_timestamp_ranges, where="up")
with pytest.raises(AssertionError):
cc["0ns":"20ns"].downsampled_over(stack[3:4].frame_timestamp_ranges)
with pytest.raises(AssertionError):
cc["40ns":"70ns"].downsampled_over(stack[0:1].frame_timestamp_ranges)
assert stack[0]._get_frame(0).start == 10
assert stack[1]._get_frame(0).start == 20
assert stack[1:3]._get_frame(0).start == 20
assert stack[1:3]._get_frame(0).start == 20
assert stack[1:3]._get_frame(1).start == 30
# Regression test downsampled_over losing precision due to reverting to double rather than int64.
cc = channel.Slice(
channel.Continuous(np.arange(10, 80, 2), 1588267266006287100, 1000))
ch = cc.downsampled_over([(1588267266006287100, 1588267266006287120)],
where="left")
assert int(ch.timestamps[0]) == 1588267266006287100
