def test_cancel(self):
"""
Test cancelling does cancel (relatively quickly)
"""
self.focus.moveAbs({"z": self._good_focus - 200e-6}).result()
data = tiff.read_data(
os.path.join(TEST_IMAGE_PATH,
"brightlight-off-slit-spccd-simple.ome.tiff"))
new_img = img.ensure2DImage(data[0])
self.spccd.set_image(new_img)
f = Sparc2AutoFocus("spec-focus", self.optmngr, [self.specline_ccd],
True)
time.sleep(5)
data = tiff.read_data(
os.path.join(TEST_IMAGE_PATH,
"brightlight-on-slit-spccd-simple.ome.tiff"))
new_img = img.ensure2DImage(data[0])
self.spccd.set_image(new_img)
cancelled = f.cancel()
self.assertTrue(cancelled)
self.assertTrue(f.cancelled())
with self.assertRaises(CancelledError):
res = f.result(timeout=900)
def test_one_det(self):
"""
Test AutoFocus Spectrometer on CCD
"""
self.focus.moveAbs({"z": self._good_focus - 200e-6}).result()
data = tiff.read_data(
os.path.join(TEST_IMAGE_PATH,
"brightlight-off-slit-spccd-simple.ome.tiff"))
new_img = img.ensure2DImage(data[0])
self.spccd.set_image(new_img)
f = Sparc2AutoFocus("spec-focus", self.optmngr, [self.specline_ccd],
True)
time.sleep(5)
data = tiff.read_data(
os.path.join(TEST_IMAGE_PATH,
"brightlight-on-slit-spccd-simple.ome.tiff"))
new_img = img.ensure2DImage(data[0])
self.spccd.set_image(new_img)
res = f.result(timeout=900)
for (g, d), fpos in res.items():
self.assertEqual(d.role, self.ccd.role)
self.assertAlmostEqual(fpos, self._good_focus, 3)
self.assertEqual(len(res.keys()),
len(self.spgr_ded.axes["grating"].choices))
def test_multi_det(self):
"""
Test AutoFocus Spectrometer with multiple detectors
"""
# Note: a full procedure would start by setting the slit to the smallest position
# (cf optical path mode "spec-focus") and activating an energy source
specline_mul = [self.specline_ccd, self.specline_spccd]
self.focus.moveAbs({"z": self._good_focus + 400e-6}).result()
data = tiff.read_data(
os.path.join(TEST_IMAGE_PATH,
"brightlight-off-slit-spccd-simple.ome.tiff"))
new_img = img.ensure2DImage(data[0])
self.spccd.set_image(new_img)
f = Sparc2AutoFocus("spec-focus", self.optmngr, specline_mul, True)
time.sleep(5)
data = tiff.read_data(
os.path.join(TEST_IMAGE_PATH,
"brightlight-on-slit-spccd-simple.ome.tiff"))
new_img = img.ensure2DImage(data[0])
self.spccd.set_image(new_img)
res = f.result(timeout=900)
for (g, d), fpos in res.items():
self.assertIn(d.role, (self.ccd.role, self.spccd.role))
if d.role is self.ccd.role:
self.assertAlmostEqual(fpos, self._good_focus, 3)
if d.role is self.spccd.role:
self.assertAlmostEqual(fpos, self._good_focus, 3)
# We expect an entry for each combination grating/detector
self.assertEqual(len(res.keys()),
len(self.spgr_ded.axes["grating"].choices))
def test_multi_det(self):
"""
Test AutoFocus Spectrometer with multiple detectors
"""
# Note: a full procedure would start by setting the slit to the smallest position
# (cf optical path mode "spec-focus") and activating an energy source
specline_mul = [self.specline_ccd, self.specline_spccd]
self.focus.moveAbs({"z": self._good_focus + 400e-6}).result()
data = tiff.read_data(os.path.join(TEST_IMAGE_PATH, "brightlight-off-slit-spccd-simple.ome.tiff"))
new_img = img.ensure2DImage(data[0])
self.spccd.set_image(new_img)
f = Sparc2AutoFocus("spec-focus", self.optmngr, specline_mul, True)
time.sleep(5)
data = tiff.read_data(os.path.join(TEST_IMAGE_PATH, "brightlight-on-slit-spccd-simple.ome.tiff"))
new_img = img.ensure2DImage(data[0])
self.spccd.set_image(new_img)
res = f.result(timeout=900)
for (g, d), fpos in res.items():
self.assertIn(d.role, (self.ccd.role, self.spccd.role))
if d.role is self.ccd.role:
self.assertAlmostEqual(fpos, self._good_focus, 3)
if d.role is self.spccd.role:
self.assertAlmostEqual(fpos, self._good_focus, 3)
# We expect an entry for each combination grating/detector
self.assertEqual(len(res.keys()), len(self.spgr_ded.axes["grating"].choices))
def setUp(self):
# These are example data (computer generated)
data = tiff.read_data(os.path.join(TEST_IMAGE_PATH,
"moi_input.tif"))[0]
background = tiff.read_data(
os.path.join(TEST_IMAGE_PATH, "moi_background.tif"))[0]
self.data = data
self.background = background
def setUp(self):
self.light = simulated.Light("Calibration Light", "brightlight")
self.data = tiff.read_data(os.path.join(TEST_IMAGE_PATH, "brightlight-off-slit-spccd-simple.ome.tiff"))
self.img_spccd_loff = img.ensure2DImage(self.data[0])
self.data = tiff.read_data(os.path.join(TEST_IMAGE_PATH, "brightlight-on-slit-spccd-simple.ome.tiff"))
self.img_spccd_lon = img.ensure2DImage(self.data[0])
self.data = tiff.read_data(os.path.join(TEST_IMAGE_PATH, "brightlight-off-slit-ccd.ome.tiff"))
self.img_ccd_loff = img.ensure2DImage(self.data[0])
self.data = tiff.read_data(os.path.join(TEST_IMAGE_PATH, "brightlight-on-slit-ccd.ome.tiff"))
self.img_ccd_lon = img.ensure2DImage(self.data[0])
def test_autofocus_slit(self):
"""
Test AutoFocus on 1 line CCD for an image of a slit.
"""
# Change image to slit image.
data = tiff.read_data(
os.path.join(TEST_IMAGE_PATH,
"brightlight-on-slit-spccd-simple.ome.tiff"))
new_img = img.ensure2DImage(data[0])
self.ccd.set_image(new_img)
self.spectrometer.binning.value = (4, 64)
self.focus.moveAbs({"z": self._good_focus - 200e-6}).result()
f = align.AutoFocus(self.spectrometer,
None,
self.focus,
method=MTD_BINARY)
foc_pos, foc_lev = f.result(timeout=900)
logging.debug("Found focus at {} good focus at {}".format(
foc_pos, self._good_focus))
# The focus step size is 10.9e-6, the tolerance is set to 2.5e-5; approximately two focus steps.
numpy.testing.assert_allclose(foc_pos, self._good_focus, atol=2.5e-5)
self.focus.moveAbs({"z": self._good_focus + 400e-6}).result()
f = align.AutoFocus(self.spectrometer,
None,
self.focus,
method=MTD_BINARY)
foc_pos, foc_lev = f.result(timeout=900)
logging.debug("Found focus at {} good focus at {}".format(
foc_pos, self._good_focus))
# The focus step size is 10.9e-6, the tolerance is set to 2.5e-5; approximately two focus steps.
numpy.testing.assert_allclose(foc_pos, self._good_focus, atol=2.5e-5)
def testReadMDOutWlBands(self):
"""
Checks that we hand MD_OUT_WL if it contains multiple bands.
OME supports only one value, so it's ok to discard some info.
"""
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "blue dye",
model.MD_ACQ_DATE: time.time() + 1,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_OUT_WL: ((650e-9, 660e-9), (675e-9, 680e-9)), # m
model.MD_USER_TINT: (255, 0, 65), # purple
model.MD_LIGHT_POWER: 100e-3 # W
},
]
size = (512, 256)
dtype = numpy.dtype("uint16")
ldata = []
for i, md in enumerate(metadata):
a = model.DataArray(numpy.zeros(size[::-1], dtype), md.copy())
a[i, i] = i # "watermark" it
a[i + 1, i + 5] = i + 1 # "watermark" it
ldata.append(a)
# export
tiff.export(FILENAME, ldata)
# check data
rdata = tiff.read_data(FILENAME)
self.assertEqual(len(rdata), len(ldata))
for i, im in enumerate(rdata):
self.assertEqual(im[i + 1, i + 5], i + 1)
im = rdata[0]
emd = metadata[0].copy()
rmd = im.metadata
img.mergeMetadata(emd)
img.mergeMetadata(rmd)
self.assertEqual(rmd[model.MD_DESCRIPTION], emd[model.MD_DESCRIPTION])
iwl = rmd[model.MD_IN_WL] # nm
self.assertTrue((emd[model.MD_IN_WL][0] <= iwl[0] and
iwl[1] <= emd[model.MD_IN_WL][-1]))
# It should be within at least one of the bands
owl = rmd[model.MD_OUT_WL] # nm
for eowl in emd[model.MD_OUT_WL]:
if (eowl[0] <= owl[0] and owl[1] <= eowl[-1]):
break
else:
self.fail("Out wl %s is not within original metadata" % (owl,))
def test_find_sh_hole_center(self):
"""
Test FindCircleCenter for holes
"""
# Real image from the DELPHI
data = tiff.read_data(os.path.join(TEST_IMAGE_PATH, "sh_hole_up.tiff"))
hole_coordinates = delphi.FindCircleCenter(data[0], delphi.HOLE_RADIUS, 6, darkest=True)
# FIXME: it fails (but not that important for calibration)
expected_coordinates = (-0.00014212, 9.405e-05) # about: 888, 934 = -0.00014212, 9.405e-05
numpy.testing.assert_almost_equal(hole_coordinates, expected_coordinates)
def test_find_sh_hole_center(self):
"""
Test FindCircleCenter for holes
"""
# Real image from the DELPHI
data = tiff.read_data(os.path.join(TEST_IMAGE_PATH, "sh_hole_up.tiff"))
hole_coordinates = delphi.FindCircleCenter(data[0], delphi.HOLE_RADIUS, 6, darkest=True)
# FIXME: it fails (but not that important for calibration)
expected_coordinates = (-0.00014212, 9.405e-05) # about: 888, 934 = -0.00014212, 9.405e-05
numpy.testing.assert_almost_equal(hole_coordinates, expected_coordinates)
def test_cancel(self):
"""
Test cancelling does cancel (relatively quickly)
"""
self.focus.moveAbs({"z": self._good_focus - 200e-6}).result()
data = tiff.read_data(os.path.join(TEST_IMAGE_PATH, "brightlight-off-slit-spccd-simple.ome.tiff"))
new_img = img.ensure2DImage(data[0])
self.spccd.set_image(new_img)
f = Sparc2AutoFocus("spec-focus", self.optmngr, [self.specline_ccd], True)
time.sleep(5)
data = tiff.read_data(os.path.join(TEST_IMAGE_PATH, "brightlight-on-slit-spccd-simple.ome.tiff"))
new_img = img.ensure2DImage(data[0])
self.spccd.set_image(new_img)
cancelled = f.cancel()
self.assertTrue(cancelled)
self.assertTrue(f.cancelled())
with self.assertRaises(CancelledError):
res = f.result(timeout=900)
def testExportReadPyramidal(self):
"""
Checks that we can read back a pyramidal image
"""
# create 2 simple greyscale images
sizes = [(512, 256), (500, 400)
] # different sizes to ensure different acquisitions
dtype = numpy.dtype("uint16")
white = (12, 52) # non symmetric position
ldata = []
num = 2
# TODO: check support for combining channels when same data shape
for i in range(num):
a = model.DataArray(numpy.zeros(sizes[i][-1:-3:-1], dtype))
a[white[-1:-3:-1]] = 1027
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 0] += 255 # red
blue = (12, 22) # non symmetric position
thumbnail[blue[-1:-3:-1]] = [0, 0, 255]
# export
tiff.export(FILENAME,
ldata,
thumbnail,
multiple_files=True,
pyramid=True)
tokens = FILENAME.split(".0.", 1)
# Iterate through the files generated
for file_index in range(num):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
# check it's here
st = os.stat(fname) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(fname)
self.assertEqual(len(rdata), num)
for i, im in enumerate(rdata):
if len(im.shape) > 2:
subim = im[0, 0, 0] # remove C,T,Z dimensions
else:
subim = im # TODO: should it always be 5 dim?
self.assertEqual(subim.shape, sizes[i][-1::-1])
self.assertEqual(subim[white[-1:-3:-1]],
ldata[i][white[-1:-3:-1]])
def test_one_det(self):
"""
Test AutoFocus Spectrometer on CCD
"""
self.focus.moveAbs({"z": self._good_focus - 200e-6}).result()
data = tiff.read_data(os.path.join(TEST_IMAGE_PATH, "brightlight-off-slit-spccd-simple.ome.tiff"))
new_img = img.ensure2DImage(data[0])
self.spccd.set_image(new_img)
f = Sparc2AutoFocus("spec-focus", self.optmngr, [self.specline_ccd], True)
time.sleep(5)
data = tiff.read_data(os.path.join(TEST_IMAGE_PATH, "brightlight-on-slit-spccd-simple.ome.tiff"))
new_img = img.ensure2DImage(data[0])
self.spccd.set_image(new_img)
res = f.result(timeout=900)
for (g, d), fpos in res.items():
self.assertEqual(d.role, self.ccd.role)
self.assertAlmostEqual(fpos, self._good_focus, 3)
self.assertEqual(len(res.keys()), len(self.spgr_ded.axes["grating"].choices))
def testExportRead(self):
"""
Checks that we can read back an image and a thumbnail
"""
# create 2 simple greyscale images
sizes = [(512, 256), (500, 400)
] # different sizes to ensure different acquisitions
dtype = numpy.dtype("uint16")
white = (12, 52) # non symmetric position
ldata = []
num = 2
# TODO: check support for combining channels when same data shape
for i in range(num):
a = model.DataArray(numpy.zeros(sizes[i][-1:-3:-1], dtype))
a[white[-1:-3:-1]] = 1027
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 0] += 255 # red
blue = (12, 22) # non symmetric position
thumbnail[blue[-1:-3:-1]] = [0, 0, 255]
# export
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
self.assertEqual(len(rdata), num)
for i, im in enumerate(rdata):
if len(im.shape) > 2:
subim = im[0, 0, 0] # remove C,T,Z dimensions
else:
subim = im # TODO: should it always be 5 dim?
self.assertEqual(subim.shape, sizes[i][-1::-1])
self.assertEqual(subim[white[-1:-3:-1]], ldata[i][white[-1:-3:-1]])
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [255, 0, 0])
self.assertEqual(im[blue[-1:-3:-1]].tolist(), [0, 0, 255])
def test_key_error(self):
image = read_data("images/super_z_single_beed_semi_in_focus.tif")[0]
# Check if the key error is raised when the key 'x' is missing
calib_data_missing_key = CALIB_DATA.copy()
_ = calib_data_missing_key.pop("x")
with self.assertRaises(KeyError):
_, _ = determine_z_position(image, calib_data_missing_key)
# Check if the key error is raised when the key 'z_calibration_range' is missing
calib_data_missing_key = CALIB_DATA.copy()
_ = calib_data_missing_key.pop("z_calibration_range")
with self.assertRaises(KeyError):
_, _ = determine_z_position(image, calib_data_missing_key)
def testExportRead(self):
"""
Checks that we can read back an image and a thumbnail
"""
# create 2 simple greyscale images
sizes = [(512, 256), (500, 400)] # different sizes to ensure different acquisitions
dtype = numpy.dtype("uint16")
white = (12, 52) # non symmetric position
ldata = []
num = 2
# TODO: check support for combining channels when same data shape
for i in range(num):
a = model.DataArray(numpy.zeros(sizes[i][-1:-3:-1], dtype))
a[white[-1:-3:-1]] = 1027
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 0] += 255 # red
blue = (12, 22) # non symmetric position
thumbnail[blue[-1:-3:-1]] = [0, 0, 255]
# export
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
self.assertEqual(len(rdata), num)
for i, im in enumerate(rdata):
if len(im.shape) > 2:
subim = im[0, 0, 0] # remove C,T,Z dimensions
else:
subim = im # TODO: should it always be 5 dim?
self.assertEqual(subim.shape, sizes[i][-1::-1])
self.assertEqual(subim[white[-1:-3:-1]], ldata[i][white[-1:-3:-1]])
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [255, 0, 0])
self.assertEqual(im[blue[-1:-3:-1]].tolist(), [0, 0, 255])
def testRGB(self):
"""
Checks that can both write and read back an RGB image
"""
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "my exported image",
model.MD_ACQ_DATE: time.time() + 1,
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_DIMS: "YXC",
},
]
# TODO: test without alpha channel and with different DIMS order
shape = (5120, 2560, 4)
dtype = numpy.dtype("uint8")
ldata = []
for i, md in enumerate(metadata):
a = model.DataArray(numpy.zeros(shape, dtype), md.copy())
a[:, :, 3] = 255 # no transparency
a[i, i] = i # "watermark" it
a[i + 1, i + 5] = i + 1 # "watermark" it
ldata.append(a)
# export
tiff.export(FILENAME, ldata)
# check data
rdata = tiff.read_data(FILENAME)
self.assertEqual(len(rdata), len(ldata))
for i, im in enumerate(rdata):
for j in range(shape[-1]):
self.assertEqual(im[i + 1, i + 5, j], i + 1)
self.assertEqual(im.shape, shape)
emd = metadata[i].copy()
rmd = im.metadata
img.mergeMetadata(emd)
img.mergeMetadata(rmd)
self.assertEqual(rmd[model.MD_DESCRIPTION], emd[model.MD_DESCRIPTION])
self.assertEqual(rmd[model.MD_DIMS], emd[model.MD_DIMS])
self.assertAlmostEqual(rmd[model.MD_POS][0], emd[model.MD_POS][0])
self.assertAlmostEqual(rmd[model.MD_POS][1], emd[model.MD_POS][1])
self.assertAlmostEqual(rmd[model.MD_PIXEL_SIZE][0], emd[model.MD_PIXEL_SIZE][0])
self.assertAlmostEqual(rmd[model.MD_PIXEL_SIZE][1], emd[model.MD_PIXEL_SIZE][1])
def testExportReadPyramidal(self):
"""
Checks that we can read back a pyramidal image
"""
# create 2 simple greyscale images
sizes = [(512, 256), (500, 400)] # different sizes to ensure different acquisitions
dtype = numpy.dtype("uint16")
white = (12, 52) # non symmetric position
ldata = []
num = 2
# TODO: check support for combining channels when same data shape
for i in range(num):
a = model.DataArray(numpy.zeros(sizes[i][-1:-3:-1], dtype))
a[white[-1:-3:-1]] = 1027
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 0] += 255 # red
blue = (12, 22) # non symmetric position
thumbnail[blue[-1:-3:-1]] = [0, 0, 255]
# export
stiff.export(FILENAME, ldata, thumbnail, pyramid=True)
tokens = FILENAME.split(".0.", 1)
# Iterate through the files generated
for file_index in range(num):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
# check it's here
st = os.stat(fname) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(fname)
self.assertEqual(len(rdata), num)
for i, im in enumerate(rdata):
if len(im.shape) > 2:
subim = im[0, 0, 0] # remove C,T,Z dimensions
else:
subim = im # TODO: should it always be 5 dim?
self.assertEqual(subim.shape, sizes[i][-1::-1])
self.assertEqual(subim[white[-1:-3:-1]], ldata[i][white[-1:-3:-1]])
def test_determine_z_position(self):
"""
Test for known data the outcome of the function determine_z_position
"""
# Test on an image below focus
image = read_data(
"images/super_z_single_beed_aprox_500nm_under_focus.tif")[0]
expected_outcome_image_1 = -592.5e-9 # Value determined using the function determine_z_position
z, warning = determine_z_position(image, CALIB_DATA)
self.assertEqual(warning, None)
self.assertAlmostEqual(expected_outcome_image_1, z, delta=PRECISION)
# Test on an image which is roughly in focus/point of least confusion
image = read_data("images/super_z_single_beed_semi_in_focus.tif")[0]
expected_outcome_image_2 = -62.8e-9 # Value determined using the function determine_z_position
z, warning = determine_z_position(image, CALIB_DATA)
self.assertEqual(warning, None)
self.assertAlmostEqual(expected_outcome_image_2, z, delta=PRECISION)
# Test on an image which is above focus
image = read_data(
"images/super_z_single_beed_aprox_500nm_above_focus.tif")[0]
expected_outcome_image_3 = 420.6e-9 # Value determined using the function determine_z_position
z, warning = determine_z_position(image, CALIB_DATA)
self.assertEqual(warning, None)
self.assertAlmostEqual(expected_outcome_image_3, z, delta=PRECISION)
# Test on an image where no feature visible because it is just white noise
image = read_data("images/super_z_no_beed_just_noise.tif")[0]
_, warning = determine_z_position(image, CALIB_DATA)
self.assertEqual(
warning, 5
) # Since the entire image is noise the warning raised should be 5
# Test on an image where no feature visible because it is entirely white
image = read_data("images/super_z_no_beed_just_white.tif")[0]
_, warning = determine_z_position(image, CALIB_DATA)
self.assertEqual(
warning, 6
) # Since the entire image is white the warning raised should be 5
# Change the range so warning 6 is raised with an image which is just above focus
calib_data_limited_range = CALIB_DATA.copy()
calib_data_limited_range["z_calibration_range"] = (-1e-10, 1e-10)
image = read_data(
"images/super_z_single_beed_aprox_500nm_above_focus.tif")[0]
expected_outcome_image_3 = 420.6e-9 # Value determined using the function determine_z_position
z, warning = determine_z_position(image, calib_data_limited_range)
# Since the range is set to small the detected feature is to big and warning raised should be 4
self.assertEqual(warning, 4)
self.assertAlmostEqual(expected_outcome_image_3, z, delta=PRECISION)
def setUp(self):
self.imgdata = tiff.read_data('spotdata.tif')
self.coords0 = numpy.genfromtxt('spotdata.csv', delimiter=',')
def testExportNoWL(self):
"""
Check it's possible to export/import a spectrum with missing wavelength
info
"""
dtype = numpy.dtype("uint16")
size3d = (512, 256, 220) # X, Y, C
size = (512, 256)
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "bad spec",
model.MD_DESCRIPTION: "test3d",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_WL_POLYNOMIAL: [0], # m, m/px: missing polynomial
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, #s
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: u"", # check empty unicode strings
model.MD_DESCRIPTION: u"tÉst", # tiff doesn't support É (but XML does)
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, #s
model.MD_IN_WL: (500e-9, 520e-9), #m
}]
ldata = []
# 3D data generation (+ metadata): gradient along the wavelength
data3d = numpy.empty(size3d[::-1], dtype=dtype)
end = 2 ** metadata[0][model.MD_BPP]
step = end // size3d[2]
lin = numpy.arange(0, end, step, dtype=dtype)[:size3d[2]]
lin.shape = (size3d[2], 1, 1) # to be able to copy it on the first dim
data3d[:] = lin
# introduce Time and Z dimension to state the 3rd dim is channel
data3d = data3d[:, numpy.newaxis, numpy.newaxis, :, :]
ldata.append(model.DataArray(data3d, metadata[0]))
# an additional 2D data, for the sake of it
ldata.append(model.DataArray(numpy.zeros(size[::-1], dtype), metadata[1]))
# export
tiff.export(FILENAME, ldata)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
rdata = tiff.read_data(FILENAME)
self.assertEqual(len(rdata), len(ldata))
for i, im in enumerate(rdata):
md = metadata[i]
self.assertEqual(im.metadata[model.MD_DESCRIPTION], md[model.MD_DESCRIPTION])
numpy.testing.assert_allclose(im.metadata[model.MD_POS], md[model.MD_POS], rtol=1e-4)
numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE], md[model.MD_PIXEL_SIZE])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE], md[model.MD_ACQ_DATE], delta=1)
self.assertEqual(im.metadata[model.MD_BPP], md[model.MD_BPP])
self.assertEqual(im.metadata[model.MD_BINNING], md[model.MD_BINNING])
if model.MD_WL_POLYNOMIAL in md:
pn = md[model.MD_WL_POLYNOMIAL]
# either identical, or nothing at all
if model.MD_WL_POLYNOMIAL in im.metadata:
numpy.testing.assert_allclose(im.metadata[model.MD_WL_POLYNOMIAL], pn)
else:
self.assertNotIn(model.MD_WL_LIST, im.metadata)
def test_data_to_stream(self):
"""
Check data_to_static_streams
"""
FILENAME = u"test" + tiff.EXTENSIONS[0]
# Create fake data of flurorescence acquisition
metadata = [
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "sem",
model.MD_ACQ_DATE: time.time() - 1,
model.MD_BPP: 16,
model.MD_PIXEL_SIZE: (1e-7, 1e-7), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_DWELL_TIME: 100e-6, # s
model.MD_LENS_MAG: 1200, # ratio
},
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "brightfield",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (400e-9, 630e-9), # m
model.MD_OUT_WL: (400e-9, 630e-9), # m
# correction metadata
model.MD_POS_COR: (-1e-6, 3e-6), # m
model.MD_PIXEL_SIZE_COR: (1.2, 1.2),
model.MD_ROTATION_COR: 6.27, # rad
model.MD_SHEAR_COR: 0.005,
},
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "blue dye",
model.MD_ACQ_DATE: time.time() + 1,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_OUT_WL: (650e-9, 660e-9, 675e-9, 678e-9, 680e-9), # m
model.MD_USER_TINT: (255, 0, 65), # purple
model.MD_LIGHT_POWER: 100e-3 # W
},
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "green dye",
model.MD_ACQ_DATE: time.time() + 2,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1, # s
model.MD_IN_WL: (600e-9, 620e-9), # m
model.MD_OUT_WL: (620e-9, 650e-9), # m
model.MD_ROTATION: 0.1, # rad
model.MD_SHEAR: 0,
},
{
model.MD_SW_VERSION:
"1.0-test",
model.MD_HW_NAME:
"fake hw",
model.MD_DESCRIPTION:
"green dye",
model.MD_ACQ_DATE:
time.time() + 2,
model.MD_BPP:
12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME:
1, # s
model.MD_IN_WL: (600e-9, 620e-9), # m
model.MD_OUT_WL: (620e-9, 650e-9), # m
# In order to test shear is applied even without rotation
# provided. And also check that *_COR is merged into its
# normal metadata brother.
# model.MD_SHEAR: 0.03,
model.MD_SHEAR_COR:
0.003,
},
]
# create 3 greyscale images of same size
size = (512, 256)
dtype = numpy.dtype("uint16")
ldata = []
for i, md in enumerate(metadata):
a = model.DataArray(numpy.zeros(size[::-1], dtype), md.copy())
a[i, i] = i # "watermark" it
ldata.append(a)
tiff.export(FILENAME, ldata)
# check data
rdata = tiff.read_data(FILENAME)
sts = data_to_static_streams(rdata)
# There should be 5 streams: 3 fluo + 1 SEM + 1 Brightfield
fluo = bright = sem = 0
for s in sts:
if isinstance(s, stream.StaticFluoStream):
fluo += 1
elif isinstance(s, stream.StaticBrightfieldStream):
bright += 1
elif isinstance(s, stream.EMStream):
sem += 1
self.assertEqual(fluo, 3)
self.assertEqual(bright, 1)
self.assertEqual(sem, 1)
def setUp(self):
# These are example data (computer generated)
data = tiff.read_data("moi_input.tif")[0]
background = tiff.read_data("moi_background.tif")[0]
self.data = data
self.background = background
def testReadMDTime(self):
"""
Checks that we can read back the metadata of an acquisition with time correlation
"""
shapes = [(512, 256), (1, 5220, 1, 50, 40), (1, 512, 1, 1, 1)]
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "test",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_LENS_MAG: 1200, # ratio
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake time correlator",
model.MD_DESCRIPTION: "test3d",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 16,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-6), # m/px
model.MD_PIXEL_DUR: 1e-9, # s
model.MD_TIME_OFFSET:-20e-9, # s, of the first time value
model.MD_OUT_WL: "pass-through",
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake time correlator",
model.MD_DESCRIPTION: "test1d",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 16,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_PIXEL_DUR: 10e-9, # s
model.MD_TIME_OFFSET:-500e-9, # s, of the first time value
model.MD_OUT_WL: (500e-9, 600e-9),
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
},
]
# create 1 simple greyscale image
ldata = []
a = model.DataArray(numpy.zeros(shapes[0], numpy.uint16), metadata[0])
ldata.append(a)
# Create 2D time correlated image
a = model.DataArray(numpy.zeros(shapes[1], numpy.uint32), metadata[1])
a[:, :, :, 1, 5] = 1
a[0, 10, 0, 1, 0] = 10000
ldata.append(a)
# Create time correlated spot acquisition
a = model.DataArray(numpy.zeros(shapes[2], numpy.uint32), metadata[2])
a[0, 10, 0, 0, 0] = 20000
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (400, 300, 3)
thumbnail = model.DataArray(numpy.zeros(tshape, numpy.uint8))
thumbnail[:, :, 1] += 255 # green
# export
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
self.assertEqual(len(rdata), len(ldata))
for i, im in enumerate(rdata):
md = metadata[i]
self.assertEqual(im.metadata[model.MD_DESCRIPTION], md[model.MD_DESCRIPTION])
self.assertAlmostEqual(im.metadata[model.MD_POS][0], md[model.MD_POS][0])
self.assertAlmostEqual(im.metadata[model.MD_POS][1], md[model.MD_POS][1])
self.assertAlmostEqual(im.metadata[model.MD_PIXEL_SIZE][0], md[model.MD_PIXEL_SIZE][0])
self.assertAlmostEqual(im.metadata[model.MD_PIXEL_SIZE][1], md[model.MD_PIXEL_SIZE][1])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE], md[model.MD_ACQ_DATE], delta=1)
if model.MD_LENS_MAG in md:
self.assertEqual(im.metadata[model.MD_LENS_MAG], md[model.MD_LENS_MAG])
# None of the images are using light => no MD_IN_WL
self.assertFalse(model.MD_IN_WL in im.metadata,
"Reporting excitation wavelength while there is none")
if model.MD_PIXEL_DUR in md:
pxd = md[model.MD_PIXEL_DUR]
self.assertAlmostEqual(im.metadata[model.MD_PIXEL_DUR], pxd)
if model.MD_TIME_OFFSET in md:
tof = md[model.MD_TIME_OFFSET]
self.assertAlmostEqual(im.metadata[model.MD_TIME_OFFSET], tof)
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [0, 255, 0])
def testReadMDAR(self):
"""
Checks that we can read back the metadata of an Angular Resolved image
"""
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "sem survey",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_LENS_MAG: 1200, # ratio
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake ccd",
model.MD_DESCRIPTION: "AR",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_SENSOR_PIXEL_SIZE: (13e-6, 13e-6), # m/px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1.2e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_AR_POLE: (253.1, 65.1), # px
model.MD_AR_XMAX: 12e-3,
model.MD_AR_HOLE_DIAMETER: 0.6e-3,
model.MD_AR_FOCUS_DISTANCE: 0.5e-3,
model.MD_AR_PARABOLA_F: 2e-3,
model.MD_LENS_MAG: 60, # ratio
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake ccd",
model.MD_DESCRIPTION: "AR",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_SENSOR_PIXEL_SIZE: (13e-6, 13e-6), # m/px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_AR_POLE: (253.1, 65.1), # px
model.MD_AR_XMAX: 12e-3,
model.MD_AR_HOLE_DIAMETER: 0.6e-3,
model.MD_AR_FOCUS_DISTANCE: 0.5e-3,
model.MD_AR_PARABOLA_F: 2e-3,
model.MD_LENS_MAG: 60, # ratio
},
]
# create 2 simple greyscale images
sizes = [(512, 256), (500, 400), (500, 400)] # different sizes to ensure different acquisitions
dtype = numpy.dtype("uint16")
ldata = []
for s, md in zip(sizes, metadata):
a = model.DataArray(numpy.zeros(s[::-1], dtype), md)
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 1] += 255 # green
# export
stiff.export(FILENAME, ldata, thumbnail)
tokens = FILENAME.split(".0.", 1)
self.no_of_images = 2
# Iterate through the files generated
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
# check it's here
st = os.stat(fname) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(fname)
self.assertEqual(len(rdata), len(ldata))
for im, md in zip(rdata, metadata):
self.assertEqual(im.metadata[model.MD_DESCRIPTION], md[model.MD_DESCRIPTION])
numpy.testing.assert_allclose(im.metadata[model.MD_POS], md[model.MD_POS], rtol=1e-4)
numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE], md[model.MD_PIXEL_SIZE])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE], md[model.MD_ACQ_DATE], delta=1)
if model.MD_AR_POLE in md:
numpy.testing.assert_allclose(im.metadata[model.MD_AR_POLE], md[model.MD_AR_POLE])
if model.MD_AR_XMAX in md:
self.assertAlmostEqual(im.metadata[model.MD_AR_XMAX], md[model.MD_AR_XMAX])
if model.MD_AR_HOLE_DIAMETER in md:
self.assertAlmostEqual(im.metadata[model.MD_AR_HOLE_DIAMETER], md[model.MD_AR_HOLE_DIAMETER])
if model.MD_AR_FOCUS_DISTANCE in md:
self.assertAlmostEqual(im.metadata[model.MD_AR_FOCUS_DISTANCE], md[model.MD_AR_FOCUS_DISTANCE])
if model.MD_AR_PARABOLA_F in md:
self.assertAlmostEqual(im.metadata[model.MD_AR_PARABOLA_F], md[model.MD_AR_PARABOLA_F])
if model.MD_LENS_MAG in md:
self.assertAlmostEqual(im.metadata[model.MD_LENS_MAG], md[model.MD_LENS_MAG])
def testReadMDFluo(self):
"""
Checks that we can read back the metadata of a fluoresence image
The OME-TIFF file will contain just one big array, but three arrays
should be read back with the right data.
"""
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "brightfield",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (400e-9, 630e-9), # m
model.MD_OUT_WL: (400e-9, 630e-9), # m
# correction metadata
model.MD_POS_COR: (-1e-6, 3e-6), # m
model.MD_PIXEL_SIZE_COR: (1.2, 1.2),
model.MD_ROTATION_COR: 6.27, # rad
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "blue dye",
model.MD_ACQ_DATE: time.time() + 1,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_OUT_WL: (600e-9, 630e-9), # m
model.MD_USER_TINT: (255, 0, 65) # purple
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "green dye",
model.MD_ACQ_DATE: time.time() + 2,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1, # s
model.MD_IN_WL: (600e-9, 620e-9), # m
model.MD_OUT_WL: (620e-9, 650e-9), # m
model.MD_ROTATION: 0.1, # rad
},
]
# create 3 greyscale images of same size
size = (512, 256)
dtype = numpy.dtype("uint16")
ldata = []
for i, md in enumerate(metadata):
a = model.DataArray(numpy.zeros(size[::-1], dtype), md)
a[i, i] = i # "watermark" it
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (size[1] // 8, size[0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 1] += 255 # green
# export
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
self.assertEqual(len(rdata), len(ldata))
# TODO: rdata and ldata don't have to be in the same order
for i, im in enumerate(rdata):
md = metadata[i].copy()
img.mergeMetadata(md)
self.assertEqual(im.metadata[model.MD_DESCRIPTION], md[model.MD_DESCRIPTION])
numpy.testing.assert_allclose(im.metadata[model.MD_POS], md[model.MD_POS], rtol=1e-4)
numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE], md[model.MD_PIXEL_SIZE])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE], md[model.MD_ACQ_DATE], delta=1)
self.assertEqual(im.metadata[model.MD_BPP], md[model.MD_BPP])
self.assertEqual(im.metadata[model.MD_BINNING], md[model.MD_BINNING])
if model.MD_USER_TINT in md:
self.assertEqual(im.metadata[model.MD_USER_TINT], md[model.MD_USER_TINT])
iwl = im.metadata[model.MD_IN_WL] # nm
self.assertTrue((md[model.MD_IN_WL][0] <= iwl[0] and
iwl[1] <= md[model.MD_IN_WL][1]))
owl = im.metadata[model.MD_OUT_WL] # nm
self.assertTrue((md[model.MD_OUT_WL][0] <= owl[0] and
owl[1] <= md[model.MD_OUT_WL][1]))
self.assertAlmostEqual(im.metadata.get(model.MD_ROTATION, 0), md.get(model.MD_ROTATION, 0))
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [0, 255, 0])
def testReadMDSpec(self):
"""
Checks that we can read back the metadata of a spectrum image
"""
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "test",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_OUT_WL: (600e-9, 630e-9), # m
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake spec",
model.MD_DESCRIPTION: "test3d",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_WL_POLYNOMIAL: [500e-9, 1e-9], # m, m/px: wl polynomial
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
},
]
# create 2 simple greyscale images
sizes = [(512, 256), (500, 400, 1, 1, 220)] # different sizes to ensure different acquisitions
dtype = numpy.dtype("uint8")
ldata = []
for i, s in enumerate(sizes):
a = model.DataArray(numpy.zeros(s[::-1], dtype), metadata[i])
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 1] += 255 # green
# export
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
self.assertEqual(len(rdata), len(ldata))
for i, im in enumerate(rdata):
md = metadata[i]
self.assertEqual(im.metadata[model.MD_DESCRIPTION], md[model.MD_DESCRIPTION])
numpy.testing.assert_allclose(im.metadata[model.MD_POS], md[model.MD_POS], rtol=1e-4)
numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE], md[model.MD_PIXEL_SIZE])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE], md[model.MD_ACQ_DATE], delta=1)
self.assertEqual(im.metadata[model.MD_BPP], md[model.MD_BPP])
self.assertEqual(im.metadata[model.MD_BINNING], md[model.MD_BINNING])
if model.MD_WL_POLYNOMIAL in md:
pn = md[model.MD_WL_POLYNOMIAL]
# 2 formats possible
if model.MD_WL_LIST in im.metadata:
l = ldata[i].shape[0]
npn = polynomial.Polynomial(pn,
domain=[0, l - 1],
window=[0, l - 1])
wl = npn.linspace(l)[1]
numpy.testing.assert_allclose(im.metadata[model.MD_WL_LIST], wl)
else:
numpy.testing.assert_allclose(im.metadata[model.MD_WL_POLYNOMIAL], pn)
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [0, 255, 0])
def testReadMDFluo(self):
"""
Checks that we can read back the metadata of a fluoresence image
The OME-TIFF file will contain just one big array, but three arrays
should be read back with the right data.
"""
metadata = [
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "brightfield",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (400e-9, 630e-9), # m
model.MD_OUT_WL: (400e-9, 630e-9), # m
# correction metadata
model.MD_POS_COR: (-1e-6, 3e-6), # m
model.MD_PIXEL_SIZE_COR: (1.2, 1.2),
model.MD_ROTATION_COR: 6.27, # rad
},
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "blue dye",
model.MD_ACQ_DATE: time.time() + 1,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_OUT_WL: (600e-9, 630e-9), # m
model.MD_USER_TINT: (255, 0, 65) # purple
},
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "green dye",
model.MD_ACQ_DATE: time.time() + 2,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1, # s
model.MD_IN_WL: (600e-9, 620e-9), # m
model.MD_OUT_WL: (620e-9, 650e-9), # m
model.MD_ROTATION: 0.1, # rad
},
]
# create 3 greyscale images of same size
size = (512, 256)
dtype = numpy.dtype("uint16")
ldata = []
for i, md in enumerate(metadata):
a = model.DataArray(numpy.zeros(size[::-1], dtype), md)
a[i, i] = i # "watermark" it
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (size[1] // 8, size[0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 1] += 255 # green
# export
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
self.assertEqual(len(rdata), len(ldata))
# TODO: rdata and ldata don't have to be in the same order
for i, im in enumerate(rdata):
md = metadata[i].copy()
img.mergeMetadata(md)
self.assertEqual(im.metadata[model.MD_DESCRIPTION],
md[model.MD_DESCRIPTION])
numpy.testing.assert_allclose(im.metadata[model.MD_POS],
md[model.MD_POS],
rtol=1e-4)
numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE],
md[model.MD_PIXEL_SIZE])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE],
md[model.MD_ACQ_DATE],
delta=1)
self.assertEqual(im.metadata[model.MD_BPP], md[model.MD_BPP])
self.assertEqual(im.metadata[model.MD_BINNING],
md[model.MD_BINNING])
if model.MD_USER_TINT in md:
self.assertEqual(im.metadata[model.MD_USER_TINT],
md[model.MD_USER_TINT])
iwl = im.metadata[model.MD_IN_WL] # nm
self.assertTrue((md[model.MD_IN_WL][0] <= iwl[0]
and iwl[1] <= md[model.MD_IN_WL][1]))
owl = im.metadata[model.MD_OUT_WL] # nm
self.assertTrue((md[model.MD_OUT_WL][0] <= owl[0]
and owl[1] <= md[model.MD_OUT_WL][1]))
self.assertAlmostEqual(im.metadata.get(model.MD_ROTATION, 0),
md.get(model.MD_ROTATION, 0))
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [0, 255, 0])
def testReadMDAR(self):
"""
Checks that we can read back the metadata of an Angular Resolved image
"""
metadata = [
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "sem survey",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_LENS_MAG: 1200, # ratio
},
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake ccd",
model.MD_DESCRIPTION: "AR",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_SENSOR_PIXEL_SIZE: (13e-6, 13e-6), # m/px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1.2e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_AR_POLE: (253.1, 65.1), # px
model.MD_LENS_MAG: 60, # ratio
},
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake ccd",
model.MD_DESCRIPTION: "AR",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_SENSOR_PIXEL_SIZE: (13e-6, 13e-6), # m/px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_AR_POLE: (253.1, 65.1), # px
model.MD_LENS_MAG: 60, # ratio
},
]
# create 2 simple greyscale images
sizes = [(512, 256), (500, 400), (500, 400)
] # different sizes to ensure different acquisitions
dtype = numpy.dtype("uint16")
ldata = []
for s, md in zip(sizes, metadata):
a = model.DataArray(numpy.zeros(s[::-1], dtype), md)
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 1] += 255 # green
# export
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
self.assertEqual(len(rdata), len(ldata))
for im, md in zip(rdata, metadata):
self.assertEqual(im.metadata[model.MD_DESCRIPTION],
md[model.MD_DESCRIPTION])
numpy.testing.assert_allclose(im.metadata[model.MD_POS],
md[model.MD_POS],
rtol=1e-4)
numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE],
md[model.MD_PIXEL_SIZE])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE],
md[model.MD_ACQ_DATE],
delta=1)
if model.MD_AR_POLE in md:
numpy.testing.assert_allclose(im.metadata[model.MD_AR_POLE],
md[model.MD_AR_POLE])
if model.MD_LENS_MAG in md:
self.assertAlmostEqual(im.metadata[model.MD_LENS_MAG],
md[model.MD_LENS_MAG])
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [0, 255, 0])
def testReadMDSpec(self):
"""
Checks that we can read back the metadata of a spectrum image
"""
sizes = [(512, 256), (500, 400, 1, 1, 220)
] # different sizes to ensure different acquisitions
metadata = [
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "test",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_OUT_WL: (650e-9, 660e-9, 675e-9, 678e-9, 680e-9), # m
},
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake spec",
model.MD_DESCRIPTION: "test3d",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_WL_LIST:
[500e-9 + i * 1e-9 for i in range(sizes[1][-1])],
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
},
]
# create 2 simple greyscale images
dtype = numpy.dtype("uint8")
ldata = []
for i, s in enumerate(sizes):
a = model.DataArray(numpy.zeros(s[::-1], dtype), metadata[i])
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 1] += 255 # green
# export
stiff.export(FILENAME, ldata, thumbnail)
tokens = FILENAME.split(".0.", 1)
self.no_of_images = 2
# Iterate through the files generated
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
# check it's here
st = os.stat(fname) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(fname)
self.assertEqual(len(rdata), len(ldata))
for i, im in enumerate(rdata):
md = metadata[i]
self.assertEqual(im.metadata[model.MD_DESCRIPTION],
md[model.MD_DESCRIPTION])
numpy.testing.assert_allclose(im.metadata[model.MD_POS],
md[model.MD_POS],
rtol=1e-4)
numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE],
md[model.MD_PIXEL_SIZE])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE],
md[model.MD_ACQ_DATE],
delta=1)
self.assertEqual(im.metadata[model.MD_BPP], md[model.MD_BPP])
self.assertEqual(im.metadata[model.MD_BINNING],
md[model.MD_BINNING])
def testReadMDMnchr(self):
"""
Checks that we can read back the metadata of a monochromator image.
It's 32 bits, and the same shape as the ETD
"""
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake monochromator",
model.MD_SAMPLES_PER_PIXEL: 1,
model.MD_DESCRIPTION: "test",
model.MD_ACQ_DATE: time.time(),
model.MD_HW_VERSION: "Unknown",
model.MD_DWELL_TIME: 0.001, # s
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (1.2e-3, -30e-3), # m
model.MD_LENS_MAG: 100, # ratio
model.MD_OUT_WL: (2.8e-07, 3.1e-07)
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_VERSION: "Unknown",
model.MD_SAMPLES_PER_PIXEL: 1,
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "etd",
model.MD_ACQ_DATE: time.time(),
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_LENS_MAG: 100, # ratio
model.MD_DWELL_TIME: 1e-06, # s
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_VERSION: "Unknown",
model.MD_SAMPLES_PER_PIXEL: 1,
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "Anchor region",
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (10e-3, 30e-3), # m
model.MD_LENS_MAG: 100, # ratio
model.MD_AD_LIST: (1437117571.733935, 1437117571.905051),
model.MD_DWELL_TIME: 1e-06, # s
},
]
# create 3 greyscale images
ldata = []
mnchr_size = (6, 5)
sem_size = (128, 128)
# Monochromator
mnchr_dtype = numpy.dtype("uint32")
a = model.DataArray(numpy.zeros(mnchr_size[::-1], mnchr_dtype), metadata[0])
ldata.append(a)
# Normal SEM
sem_dtype = numpy.dtype("uint16")
b = model.DataArray(numpy.zeros(mnchr_size[::-1], sem_dtype), metadata[1])
ldata.append(b)
# Anchor data
c = model.DataArray(numpy.zeros(sem_size[::-1], sem_dtype), metadata[2])
ldata.append(c)
# export
tiff.export(FILENAME, ldata)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
self.assertEqual(len(rdata), len(ldata))
for i, im in enumerate(rdata):
md = metadata[i].copy()
img.mergeMetadata(md)
self.assertEqual(im.metadata[model.MD_DESCRIPTION], md[model.MD_DESCRIPTION])
self.assertAlmostEqual(im.metadata[model.MD_POS][0], md[model.MD_POS][0])
self.assertAlmostEqual(im.metadata[model.MD_POS][1], md[model.MD_POS][1])
self.assertAlmostEqual(im.metadata[model.MD_PIXEL_SIZE][0], md[model.MD_PIXEL_SIZE][0])
self.assertAlmostEqual(im.metadata[model.MD_PIXEL_SIZE][1], md[model.MD_PIXEL_SIZE][1])
# Check that output wavelength range was correctly read back
owl = rdata[0].metadata[model.MD_OUT_WL] # nm
md = metadata[0]
self.assertTrue((md[model.MD_OUT_WL][0] <= owl[0] and
owl[1] <= md[model.MD_OUT_WL][-1]))
def testExportNoWL(self):
"""
Check it's possible to export/import a spectrum with missing wavelength
info
"""
dtype = numpy.dtype("uint16")
size3d = (512, 256, 220) # X, Y, C
size = (512, 256)
metadata = [
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "bad spec",
model.MD_DESCRIPTION: "test3d",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_WL_POLYNOMIAL: [0], # m, m/px: missing polynomial
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, #s
},
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: u"", # check empty unicode strings
model.MD_DESCRIPTION:
u"tÉst", # tiff doesn't support É (but XML does)
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, #s
model.MD_IN_WL: (500e-9, 520e-9), #m
}
]
ldata = []
# 3D data generation (+ metadata): gradient along the wavelength
data3d = numpy.empty(size3d[::-1], dtype=dtype)
end = 2**metadata[0][model.MD_BPP]
step = end // size3d[2]
lin = numpy.arange(0, end, step, dtype=dtype)[:size3d[2]]
lin.shape = (size3d[2], 1, 1) # to be able to copy it on the first dim
data3d[:] = lin
# introduce Time and Z dimension to state the 3rd dim is channel
data3d = data3d[:, numpy.newaxis, numpy.newaxis, :, :]
ldata.append(model.DataArray(data3d, metadata[0]))
# an additional 2D data, for the sake of it
ldata.append(
model.DataArray(numpy.zeros(size[::-1], dtype), metadata[1]))
# export
tiff.export(FILENAME, ldata)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
rdata = tiff.read_data(FILENAME)
self.assertEqual(len(rdata), len(ldata))
for i, im in enumerate(rdata):
md = metadata[i]
self.assertEqual(im.metadata[model.MD_DESCRIPTION],
md[model.MD_DESCRIPTION])
numpy.testing.assert_allclose(im.metadata[model.MD_POS],
md[model.MD_POS],
rtol=1e-4)
numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE],
md[model.MD_PIXEL_SIZE])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE],
md[model.MD_ACQ_DATE],
delta=1)
self.assertEqual(im.metadata[model.MD_BPP], md[model.MD_BPP])
self.assertEqual(im.metadata[model.MD_BINNING],
md[model.MD_BINNING])
if model.MD_WL_POLYNOMIAL in md:
pn = md[model.MD_WL_POLYNOMIAL]
# either identical, or nothing at all
if model.MD_WL_POLYNOMIAL in im.metadata:
numpy.testing.assert_allclose(
im.metadata[model.MD_WL_POLYNOMIAL], pn)
else:
self.assertNotIn(model.MD_WL_LIST, im.metadata)
def testReadMDAR(self):
"""
Checks that we can read back the metadata of an Angular Resolved image
"""
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "sem survey",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_LENS_MAG: 1200, # ratio
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake ccd",
model.MD_DESCRIPTION: "AR",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_SENSOR_PIXEL_SIZE: (13e-6, 13e-6), # m/px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1.2e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_AR_POLE: (253.1, 65.1), # px
model.MD_LENS_MAG: 60, # ratio
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake ccd",
model.MD_DESCRIPTION: "AR",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_SENSOR_PIXEL_SIZE: (13e-6, 13e-6), # m/px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_AR_POLE: (253.1, 65.1), # px
model.MD_LENS_MAG: 60, # ratio
},
]
# create 2 simple greyscale images
sizes = [(512, 256), (500, 400), (500, 400)] # different sizes to ensure different acquisitions
dtype = numpy.dtype("uint16")
ldata = []
for s, md in zip(sizes, metadata):
a = model.DataArray(numpy.zeros(s[::-1], dtype), md)
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 1] += 255 # green
# export
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
self.assertEqual(len(rdata), len(ldata))
for im, md in zip(rdata, metadata):
self.assertEqual(im.metadata[model.MD_DESCRIPTION], md[model.MD_DESCRIPTION])
numpy.testing.assert_allclose(im.metadata[model.MD_POS], md[model.MD_POS], rtol=1e-4)
numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE], md[model.MD_PIXEL_SIZE])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE], md[model.MD_ACQ_DATE], delta=1)
if model.MD_AR_POLE in md:
numpy.testing.assert_allclose(im.metadata[model.MD_AR_POLE], md[model.MD_AR_POLE])
if model.MD_LENS_MAG in md:
self.assertAlmostEqual(im.metadata[model.MD_LENS_MAG], md[model.MD_LENS_MAG])
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [0, 255, 0])
def setUp(self):
# These are example data (computer generated)
data = tiff.read_data(os.path.join(TEST_IMAGE_PATH, "moi_input.tif"))[0]
background = tiff.read_data(os.path.join(TEST_IMAGE_PATH, "moi_background.tif"))[0]
self.data = data
self.background = background
def setUp(self):
self.imgdata = tiff.read_data('spotdata.tif')
self.coords0 = numpy.genfromtxt('spotdata.csv', delimiter=',')
def testReadMDFluo(self):
"""
Checks that we can read back the metadata of a fluoresence image
The OME-TIFF file will contain just one big array, but three arrays
should be read back with the right data.
"""
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "brightfield",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (400e-9, 630e-9), # m
model.MD_OUT_WL: (400e-9, 630e-9), # m
# correction metadata
model.MD_POS_COR: (-1e-6, 3e-6), # m
model.MD_PIXEL_SIZE_COR: (1.2, 1.2),
model.MD_ROTATION_COR: 6.27, # rad
model.MD_SHEAR_COR: 0.005,
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "blue dye",
model.MD_ACQ_DATE: time.time() + 1,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 522e-9), # m
model.MD_OUT_WL: (650e-9, 660e-9, 675e-9, 678e-9, 680e-9), # m
model.MD_USER_TINT: (255, 0, 65), # purple
model.MD_LIGHT_POWER: 100e-3 # W
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "green dye",
model.MD_ACQ_DATE: time.time() + 2,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1, # s
model.MD_IN_WL: (590e-9, 620e-9), # m
model.MD_OUT_WL: (620e-9, 650e-9), # m
model.MD_ROTATION: 0.1, # rad
model.MD_SHEAR: 0,
model.MD_BASELINE: 400.0
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "green dye",
model.MD_ACQ_DATE: time.time() + 2,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1, # s
model.MD_IN_WL: (600e-9, 630e-9), # m
model.MD_OUT_WL: (620e-9, 650e-9), # m
# In order to test shear is applied even without rotation
# provided. And also check that *_COR is merged into its
# normal metadata brother.
# model.MD_SHEAR: 0.03,
model.MD_SHEAR_COR: 0.003,
},
]
# create 3 greyscale images of same size
size = (512, 256)
dtype = numpy.dtype("uint16")
ldata = []
for i, md in enumerate(metadata):
a = model.DataArray(numpy.zeros(size[::-1], dtype), md.copy())
a[i, i] = i # "watermark" it
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (size[1] // 8, size[0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 1] += 255 # green
# export
stiff.export(FILENAME, ldata, thumbnail)
tokens = FILENAME.split(".0.", 1)
self.no_of_images = 4
# Iterate through the files generated
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
# check it's here
st = os.stat(fname) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(fname)
self.assertEqual(len(rdata), len(ldata))
# TODO: rdata and ldata don't have to be in the same order
for i, im in enumerate(rdata):
md = metadata[i].copy()
img.mergeMetadata(md)
self.assertEqual(im.metadata[model.MD_DESCRIPTION], md[model.MD_DESCRIPTION])
numpy.testing.assert_allclose(im.metadata[model.MD_POS], md[model.MD_POS], rtol=1e-4)
numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE], md[model.MD_PIXEL_SIZE])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE], md[model.MD_ACQ_DATE], delta=1)
self.assertEqual(im.metadata[model.MD_BPP], md[model.MD_BPP])
self.assertEqual(im.metadata[model.MD_BINNING], md[model.MD_BINNING])
if model.MD_USER_TINT in md:
self.assertEqual(im.metadata[model.MD_USER_TINT], md[model.MD_USER_TINT])
iwl = im.metadata[model.MD_IN_WL] # nm
self.assertTrue((md[model.MD_IN_WL][0] <= iwl[0] and
iwl[1] <= md[model.MD_IN_WL][-1]))
owl = im.metadata[model.MD_OUT_WL] # nm
self.assertTrue((md[model.MD_OUT_WL][0] <= owl[0] and
owl[1] <= md[model.MD_OUT_WL][-1]))
if model.MD_LIGHT_POWER in md:
self.assertEqual(im.metadata[model.MD_LIGHT_POWER], md[model.MD_LIGHT_POWER])
self.assertAlmostEqual(im.metadata.get(model.MD_ROTATION, 0), md.get(model.MD_ROTATION, 0))
self.assertAlmostEqual(im.metadata.get(model.MD_SHEAR, 0), md.get(model.MD_SHEAR, 0))
def test_data_to_stream(self):
"""
Check data_to_static_streams
"""
FILENAME = u"test" + tiff.EXTENSIONS[0]
# Create fake data of flurorescence acquisition
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "sem",
model.MD_ACQ_DATE: time.time() - 1,
model.MD_BPP: 16,
model.MD_PIXEL_SIZE: (1e-7, 1e-7), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_DWELL_TIME: 100e-6, # s
model.MD_LENS_MAG: 1200, # ratio
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "brightfield",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (400e-9, 630e-9), # m
model.MD_OUT_WL: (400e-9, 630e-9), # m
# correction metadata
model.MD_POS_COR: (-1e-6, 3e-6), # m
model.MD_PIXEL_SIZE_COR: (1.2, 1.2),
model.MD_ROTATION_COR: 6.27, # rad
model.MD_SHEAR_COR: 0.005,
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "blue dye",
model.MD_ACQ_DATE: time.time() + 1,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_OUT_WL: (650e-9, 660e-9, 675e-9, 678e-9, 680e-9), # m
model.MD_USER_TINT: (255, 0, 65), # purple
model.MD_LIGHT_POWER: 100e-3 # W
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "green dye",
model.MD_ACQ_DATE: time.time() + 2,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1, # s
model.MD_IN_WL: (600e-9, 620e-9), # m
model.MD_OUT_WL: (620e-9, 650e-9), # m
model.MD_ROTATION: 0.1, # rad
model.MD_SHEAR: 0,
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "green dye",
model.MD_ACQ_DATE: time.time() + 2,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1, # s
model.MD_IN_WL: (600e-9, 620e-9), # m
model.MD_OUT_WL: (620e-9, 650e-9), # m
# In order to test shear is applied even without rotation
# provided. And also check that *_COR is merged into its
# normal metadata brother.
# model.MD_SHEAR: 0.03,
model.MD_SHEAR_COR: 0.003,
},
]
# create 3 greyscale images of same size
size = (512, 256)
dtype = numpy.dtype("uint16")
ldata = []
for i, md in enumerate(metadata):
a = model.DataArray(numpy.zeros(size[::-1], dtype), md.copy())
a[i, i] = i # "watermark" it
ldata.append(a)
tiff.export(FILENAME, ldata)
# check data
rdata = tiff.read_data(FILENAME)
sts = data_to_static_streams(rdata)
# There should be 5 streams: 3 fluo + 1 SEM + 1 Brightfield
fluo = bright = sem = 0
for s in sts:
if isinstance(s, stream.StaticFluoStream):
fluo += 1
elif isinstance(s, stream.StaticBrightfieldStream):
bright += 1
elif isinstance(s, stream.EMStream):
sem += 1
self.assertEqual(fluo, 3)
self.assertEqual(bright, 1)
self.assertEqual(sem, 1)
def testReadMDSpec(self):
"""
Checks that we can read back the metadata of a spectrum image
"""
metadata = [
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "test",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_OUT_WL: (600e-9, 630e-9), # m
},
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake spec",
model.MD_DESCRIPTION: "test3d",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_WL_POLYNOMIAL: [500e-9,
1e-9], # m, m/px: wl polynomial
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
},
]
# create 2 simple greyscale images
sizes = [(512, 256), (500, 400, 1, 1, 220)
] # different sizes to ensure different acquisitions
dtype = numpy.dtype("uint8")
ldata = []
for i, s in enumerate(sizes):
a = model.DataArray(numpy.zeros(s[::-1], dtype), metadata[i])
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 1] += 255 # green
# export
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
self.assertEqual(len(rdata), len(ldata))
for i, im in enumerate(rdata):
md = metadata[i]
self.assertEqual(im.metadata[model.MD_DESCRIPTION],
md[model.MD_DESCRIPTION])
numpy.testing.assert_allclose(im.metadata[model.MD_POS],
md[model.MD_POS],
rtol=1e-4)
numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE],
md[model.MD_PIXEL_SIZE])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE],
md[model.MD_ACQ_DATE],
delta=1)
self.assertEqual(im.metadata[model.MD_BPP], md[model.MD_BPP])
self.assertEqual(im.metadata[model.MD_BINNING],
md[model.MD_BINNING])
if model.MD_WL_POLYNOMIAL in md:
pn = md[model.MD_WL_POLYNOMIAL]
# 2 formats possible
if model.MD_WL_LIST in im.metadata:
l = ldata[i].shape[0]
npn = polynomial.Polynomial(pn,
domain=[0, l - 1],
window=[0, l - 1])
wl = npn.linspace(l)[1]
numpy.testing.assert_allclose(
im.metadata[model.MD_WL_LIST], wl)
else:
numpy.testing.assert_allclose(
im.metadata[model.MD_WL_POLYNOMIAL], pn)
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [0, 255, 0])
