def open_acq(fn):
"""
Read the content of an acquisition file
return (list of DataArray, list of DataArray):
list of the data in the file
thumbnail (if available, might be empty)
"""
fmt_mng = dataio.find_fittest_converter(fn, default=None, mode=os.O_RDONLY)
if fmt_mng is None:
logging.warning("Failed to find a fitting importer for file %s", fn)
# TODO: try all the formats?
fmt_mng = dataio.hdf5
if not hasattr(fmt_mng, "read_data"):
raise NotImplementedError("No support for importing format %s" % fmt_mng.FORMAT)
try:
data = fmt_mng.read_data(fn)
except Exception:
raise ValueError("Failed to open the file '%s' as %s" % (fn, fmt_mng.FORMAT))
if not data:
logging.warning("Couldn't load any data from file '%s' as %s",
fn, fmt_mng.FORMAT)
try:
thumb = fmt_mng.read_thumbnail(fn)
except Exception:
logging.exception("Failed to read the thumbnail of file '%s' as %s",
fn, fmt_mng.FORMAT)
# doesn't matter that much
thumb = []
return data, thumb
def acquire_timelapse(num, period, filename):
# find components by their role
# ebeam = model.getComponent(role="ebeam")
sed = model.getComponent(role="se-detector")
images = []
try:
for i in range(num):
logging.info("Acquiring image %d/%d", i + 1, num)
start = time.time()
images.append(sed.data.get())
left = period - (time.time() - start)
if left < 0:
logging.warning("Acquisition took longer than the period (%g s overdue)", -left)
else:
logging.info("Sleeping for another %g s", left)
time.sleep(left)
except KeyboardInterrupt:
logging.info("Closing after only %d images acquired", i + 1)
except Exception:
logging.exception("Failed to acquire all the images, will try to save anyway")
# save the file
exporter = dataio.find_fittest_converter(filename)
exporter.export(filename, images)
def save(self, dlg):
if not hasattr(self._spec_stream, "_orig_raw"):
box = wx.MessageDialog(self.main_app.main_frame,
"No correction was applied",
"No correction", wx.OK | wx.ICON_STOP)
box.ShowModal()
box.Destroy()
return
fn = self.tab_data.acq_fileinfo.value.file_name
# Store all the data present in the original file => just open it again.
das_orig = open_acquisition(fn)
das = []
for da in das_orig:
# Is it the stream that we've corrected?
if (self._spec_stream.raw[0].metadata == da.metadata and
self._spec_stream.raw[0].shape == da.shape):
das.append(self._spec_stream.raw[0])
else:
das.append(da)
# Ask for filename, with default to original filename + _corrected
# TODO: smart naming scheme if file already exists.
basefn, ext = os.path.splitext(fn)
cfn = basefn + "_corrected" + ext
cfn = ShowAcquisitionFileDialog(dlg, cfn)
exporter = dataio.find_fittest_converter(cfn)
if cfn is not None:
exporter.export(cfn, das)
else:
logging.debug("Saving cancelled")
dlg.Close()
def acquire(self, dlg):
# Stop the spot stream and any other stream playing to not interfere with the acquisition
try:
str_ctrl = self.main_app.main_data.tab.value.streambar_controller
except AttributeError: # Odemis v2.6 and earlier versions
str_ctrl = self.main_app.main_data.tab.value.stream_controller
stream_paused = str_ctrl.pauseStreams()
strs = []
if self._survey_s:
strs.append(self._survey_s)
strs.append(self._ARspectral_s)
fn = self.filename.value
exporter = dataio.find_fittest_converter(fn)
try:
f = acq.acquire(strs)
dlg.showProgress(f)
das, e = f.result() # blocks until all the acquisitions are finished
except CancelledError:
pass
finally:
pass
if not f.cancelled() and das:
if e:
logging.warning("AR spectral scan partially failed: %s", e)
logging.debug("Will save data to %s", fn)
logging.debug("Going to export data: %s", das)
exporter.export(fn, das)
dlg.Close()
def acquire_timelapse(num, period, filename):
# find components by their role
# ebeam = model.getComponent(role="ebeam")
sed = model.getComponent(role="se-detector")
images = []
try:
for i in range(num):
logging.info("Acquiring image %d/%d", i + 1, num)
start = time.time()
images.append(sed.data.get())
left = period - (time.time() - start)
if left < 0:
logging.warning(
"Acquisition took longer than the period (%g s overdue)",
-left)
else:
logging.info("Sleeping for another %g s", left)
time.sleep(left)
except KeyboardInterrupt:
logging.info("Closing after only %d images acquired", i + 1)
except Exception:
logging.exception(
"Failed to acquire all the images, will try to save anyway")
# save the file
exporter = dataio.find_fittest_converter(filename)
exporter.export(filename, images)
def test_find_fittest_converter_read(self):
# input args -> format name
test_io = [
(("coucou.h5", ), "HDF5"),
(("coucou.le monde.hdf5", ), "HDF5"),
(("coucou.H5", ), "HDF5"),
(("some/fancy/../path/file.tiff", ), "TIFF"),
(("some/fancy/../.hdf5/h5.ome.tiff", ), "TIFF"),
(("catmaids://fafb.catmaid.virtualflybrain.org/?pid=1&sid0=1", ),
"Catmaid"),
(("catmaid://catmaid.neurodata.io/catmaid/", ), "Catmaid"),
(("CATMAID://catmaid.neurodata.io/catmaid/", ), "Catmaid"),
(("a/b/d.tiff", ), "TIFF"),
(("a/b/d.ome.tiff", ), "TIFF"),
(("a/b/d.OME.tiff", ), "TIFF"),
(("a/b/d.OME.TIFF", ), "TIFF"),
(("a/b/d.h5", ), "HDF5"),
(("a/b/d.b", ), "TIFF"), # fallback to tiff
(("d.hdf5", ), "HDF5"),
(("d.HDF5", ), "HDF5"),
(("a/b/d.0.ome.tiff", ),
"TIFF"), # Serialised TIFF must be opened by TIFF
]
for args, fmt_exp in test_io:
fmt_mng = find_fittest_converter(*args, mode=os.O_RDONLY)
self.assertEqual(
fmt_mng.FORMAT, fmt_exp,
"For '%s', expected format %s but got %s" %
(args[0], fmt_exp, fmt_mng.FORMAT))
def test_find_fittest_converter_write(self):
# input args -> format name
test_io = [
(("coucou.h5", ), "HDF5"),
(("coucou.le monde.hdf5", ), "HDF5"),
(("coucou.H5", ), "HDF5"),
(("some/fancy/../path/file.tiff", ), "TIFF"),
(("some/fancy/../.hdf5/h5.ome.tiff", ), "TIFF"),
(("a/b/d.tiff", ), "TIFF"),
(("a/b/d.ome.tiff", ), "TIFF"),
(("a/b/d.OME.tiff", ), "TIFF"),
(("a/b/d.OME.TIFF", ), "TIFF"),
(("a/b/d.h5", ), "HDF5"),
(("a/b/d.b", ), "TIFF"), # fallback to tiff
(("d.hdf5", ), "HDF5"),
(("d.HDF5", ), "HDF5"),
(("a/b/d.0.ome.tiff", ), "Serialized TIFF"),
(("a/b/d.0.ome.TIFF", ), "Serialized TIFF"),
]
for args, fmt_exp in test_io:
fmt_mng = find_fittest_converter(*args)
self.assertEqual(
fmt_mng.FORMAT, fmt_exp,
"For '%s', expected format %s but got %s" %
(args[0], fmt_exp, fmt_mng.FORMAT))
def acquire(self, dlg):
# Stop the spot stream and any other stream playing to not interfere with the acquisition
str_ctrl = self._tab.streambar_controller
stream_paused = str_ctrl.pauseStreams()
strs = []
if self._survey_s:
strs.append(self._survey_s)
strs.append(self._ARspectral_s)
fn = self.filename.value
exporter = dataio.find_fittest_converter(fn)
try:
f = acqmng.acquire(strs, self.main_app.main_data.settings_obs)
dlg.showProgress(f)
das, e = f.result(
) # blocks until all the acquisitions are finished
except CancelledError:
pass
finally:
pass
if not f.cancelled() and das:
if e:
logging.warning("AR spectral scan partially failed: %s", e)
logging.debug("Will save data to %s", fn)
logging.debug("Going to export data: %s", das)
exporter.export(fn, das)
dlg.Close()
def acquire_zstack(num, interval, filename):
"""
Acquire a focus stack of num slices centered around current position, with
given interval and save to file.
"""
logging.info("Preparing to acquire z-stack of %d images with interval "
"%.3f µm giving total stack size of %.3f µm.",
num, interval, num * interval)
# find component by their role
ccd = model.getComponent(role="ccd")
focus = model.getComponent(role="focus")
origpos = focus.position.value['z']
interval *= 1.0e-6 # convert to µm
images = []
try:
for i in range(num):
islice = i - num // 2 # Make the stack centered around the origpos
pos = origpos + islice * interval
logging.info("Request move to target position %.8f", pos)
focus.moveAbs({'z': pos}).result()
logging.info("Acquiring image %d of %d", i + 1, num)
images.append(ccd.data.get())
finally:
# return to original position
focus.moveAbs({'z': origpos})
# save the file
exporter = dataio.find_fittest_converter(filename)
exporter.export(filename, images)
def __init__(self, fn, number):
self.number = number
# get the components
self.light = model.getComponent(role="light")
self.ccd = model.getComponent(role="ccd")
# TODO: only support TIFF
# prepare the data export
self.exporter = dataio.find_fittest_converter(fn)
# Make the name "fn" -> "~/Pictures/fn-XXXXXX.ext"
path, base = os.path.split(fn)
bn, ext = os.path.splitext(base)
tmpl = os.path.join(path, bn + "-%06d" + ext)
if path.startswith("/"):
# if fn starts with / => don't add ~/Pictures
self.fntmpl = tmpl
else:
self.fntmpl = os.path.join(get_picture_folder(), tmpl)
self._acq_done = threading.Event()
self._n = 0
self._startt = 0 # starting time of acquisition
self._q = Queue.Queue() # queue of tuples (str, DataArray) for saving data
# TODO: find the right number of threads, based on CPU numbers (but with
# python threading that might be a bit overkill)
for i in range(4):
t = threading.Thread(target=self._saving_thread, args=(i,))
t.daemon = True
t.start()
def save(self, dlg):
"""
Stores the current CL data into a TIFF/HDF5 file
"""
f = model.ProgressiveFuture()
try:
das = self._acquire(dlg, f)
except CancelledError:
logging.debug("Stopping acquisition + export, as it was cancelled")
return
except Exception as e:
logging.exception("Failed to acquire CL data: %s", e)
return
fn = self.filename.value
bn, ext = splitext(fn)
if ext == ".png":
logging.debug("Using HDF5 instead of PNG")
fn = bn + ".h5"
exporter = dataio.find_fittest_converter(fn)
try:
exporter.export(fn, das)
except Exception:
logging.exception("Failed to store data in %s", fn)
f.set_result(None) # Indicate it's over
self._update_filename()
def _prepare_hardware(self, ebeam_kwargs=None, ebeam_mag=2000, ccd_img=None):
if ccd_img is None:
localpath = os.path.dirname(andorcam2.__file__)
imgpath = os.path.abspath(os.path.join(localpath, "andorcam2-fake-spots-4x4.h5"))
else:
# Force absolute path, to be able to accept path relative from here
localpath = os.path.dirname(__file__)
imgpath = os.path.abspath(os.path.join(localpath, ccd_img))
fakeccd = andorcam2.AndorCam2(name="camera", role="ccd", device="fake", image=imgpath)
# Set the pixel size from the image (as there is no lens + md_updater)
converter = dataio.find_fittest_converter(imgpath, mode=os.O_RDONLY)
img = converter.read_data(imgpath)[0]
fakeccd.updateMetadata({model.MD_PIXEL_SIZE: img.metadata[model.MD_PIXEL_SIZE]})
self.ccd = fakeccd
# Force a ratio and hfw_nomag
conf_scan = CONFIG_SCANNER.copy()
if ebeam_kwargs:
conf_scan.update(ebeam_kwargs)
conf_sem = CONFIG_SEM.copy()
conf_sem["children"]["scanner"] = conf_scan
self.sem = semcomedi.SEMComedi(**conf_sem)
for child in self.sem.children.value:
if child.name == CONFIG_SED["name"]:
self.sed = child
elif child.name == CONFIG_SCANNER["name"]:
self.ebeam = child
self.ebeam.magnification.value = ebeam_mag
def __init__(self, fn, number):
self.number = number
# get the components
self.light = model.getComponent(role="light")
self.ccd = model.getComponent(role="ccd")
# TODO: only support TIFF
# prepare the data export
self.exporter = dataio.find_fittest_converter(fn)
# Make the name "fn" -> "~/Pictures/fn-XXXXXX.ext"
path, base = os.path.split(fn)
bn, ext = os.path.splitext(base)
tmpl = os.path.join(path, bn + "-%06d" + ext)
if path.startswith("/"):
# if fn starts with / => don't add ~/Pictures
self.fntmpl = tmpl
else:
self.fntmpl = os.path.join(get_picture_folder(), tmpl)
self._acq_done = threading.Event()
self._n = 0
self._startt = 0 # starting time of acquisition
self._q = queue.Queue() # queue of tuples (str, DataArray) for saving data
# TODO: find the right number of threads, based on CPU numbers (but with
# python threading that might be a bit overkill)
for i in range(4):
t = threading.Thread(target=self._saving_thread, args=(i,))
t.daemon = True
t.start()
def test_real_perfect_overlap(self):
"""
Test on decomposed image
"""
numTiles = [2, 3, 4]
overlap = [0.4]
for img in IMGS:
conv = find_fittest_converter(img)
data = conv.read_data(img)[0]
img = ensure2DImage(data)
for n in numTiles:
for o in overlap:
[tiles, _] = decompose_image(
img, o, n, "horizontalZigzag", False)
weaver = CollageWeaverReverse()
for t in tiles:
weaver.addTile(t)
sz = len(weaver.getFullImage())
w = weaver.getFullImage()
numpy.testing.assert_allclose(w, img[:sz, :sz], rtol=1)
def test_shift_real(self):
""" Test on decomposed image with known shift """
numTiles = [2, 3, 4]
overlap = [0.5, 0.4, 0.3, 0.2]
acq = ["horizontalLines", "horizontalZigzag", "verticalLines"]
for img, num, o, a in itertools.product(IMGS, numTiles, overlap, acq):
_, img_name = os.path.split(img)
conv = find_fittest_converter(img)
data = conv.read_data(img)[0]
data = ensure2DImage(data)
# Create artificial tiled image
[tiles, real_pos] = decompose_image(data, o, num, a)
px_size = tiles[0].metadata[model.MD_PIXEL_SIZE]
registrar = GlobalShiftRegistrar()
# Register tiles
for tile in tiles:
registrar.addTile(tile)
# Compare positions to real positions, allow 5 px offset
registered_pos = registrar.getPositions()[0]
diff = numpy.absolute(numpy.subtract(registered_pos, real_pos))
allowed_px_offset = numpy.repeat(numpy.multiply(px_size, 5),
len(diff))
numpy.testing.assert_array_less(
diff.flatten(), allowed_px_offset.flatten(),
"Position %s pxs off for image '%s', " %
(max(diff.flatten()) / px_size[0], img_name) +
"%s x %s tiles, %s ovlp, %s method." % (num, num, o, a))
def open_acquisition(filename, fmt=None):
"""
Opens the data according to the type of file, and returns the opened data.
If it's a pyramidal image, do not fetch the whole data from the image. If the image
is not pyramidal, it reads the entire image and returns it
filename (string): Name of the file where the image is
fmt (string): The format of the file
return (list of DataArrays or DataArrayShadows): The opened acquisition source
"""
if fmt:
converter = dataio.get_converter(fmt)
else:
converter = dataio.find_fittest_converter(filename, mode=os.O_RDONLY)
data = []
try:
if hasattr(converter, 'open_data'):
acd = converter.open_data(filename)
data = acd.content
else:
data = converter.read_data(filename)
except Exception:
logging.exception("Failed to open file '%s' with format %s", filename,
fmt)
return data
def acquire(self, dlg):
# Configure the monochromator stream according to the settings
# TODO: read the value from spotPosition instead?
self._mchr_s.emtTranslation.value = self.ebeam.translation.value
strs = []
if self._survey_s:
strs.append(self._survey_s)
strs.append(self._mchr_s)
fn = self.filename.value
exporter = dataio.find_fittest_converter(fn)
# Stop the spot stream and any other stream playing to not interfere with the acquisition
str_ctrl = self.main_app.main_data.tab.value.stream_controller
stream_paused = str_ctrl.pauseStreams()
try:
# opm is the optical path manager, that ensures the path is set to the monochromator
f = acq.acquire(strs, opm=self.main_app.main_data.opm)
dlg.showProgress(f)
das, e = f.result()
except CancelledError:
pass
finally:
str_ctrl.resumeStreams(stream_paused)
if not f.cancelled() and das:
if e:
logging.warning("Monochromator scan partially failed: %s", e)
logging.debug("Will save data to %s", fn)
exporter.export(fn, das)
self.showAcquisition(fn)
dlg.Destroy()
def acquire(self, dlg):
# Configure the monochromator stream according to the settings
# TODO: read the value from spotPosition instead?
self._mchr_s.emtTranslation.value = self.ebeam.translation.value
strs = []
if self._survey_s:
strs.append(self._survey_s)
strs.append(self._mchr_s)
fn = self.filename.value
exporter = dataio.find_fittest_converter(fn)
# Stop the spot stream and any other stream playing to not interfere with the acquisition
str_ctrl = self.main_app.main_data.tab.value.stream_controller
stream_paused = str_ctrl.pauseStreams()
try:
# opm is the optical path manager, that ensures the path is set to the monochromator
f = acq.acquire(strs, opm=self.main_app.main_data.opm)
dlg.showProgress(f)
das, e = f.result()
finally:
str_ctrl.resumeStreams(stream_paused)
if not f.cancelled() and das:
if e:
logging.warning("Monochromator scan partially failed: %s", e)
logging.debug("Will save data to %s", fn)
exporter.export(fn, das)
self.showAcquisition(fn)
dlg.Destroy()
def test_shift_real_manual(self):
""" Test case not generated by decompose.py file and manually cropped """
for img in IMGS:
conv = find_fittest_converter(img)
data = conv.read_data(img)[0]
img = ensure2DImage(data)
cropped1 = img[0:400, 0:400]
cropped2 = img[4:404, 322:722]
registrar = GlobalShiftRegistrar()
tile1 = model.DataArray(
numpy.array(cropped1),
{
model.MD_PIXEL_SIZE: [1 / 20, 1 / 20], # m/px
model.MD_POS:
(200 / 20, img.shape[1] / 20 - 200 / 20), # m
})
tile2 = model.DataArray(
numpy.array(cropped2),
{
model.MD_PIXEL_SIZE: [1 / 20, 1 / 20], # m/px
model.MD_POS:
(520 / 20, img.shape[1] / 20 - 200 / 20), # m
})
registrar.addTile(tile1)
registrar.addTile(tile2)
calculatedPositions = registrar.getPositions()[0]
diff1 = calculatedPositions[1][0] - 522 / 20
self.assertLessEqual(diff1, 1 / 20)
diff2 = calculatedPositions[1][1] - img.shape[1] / 20 - 204 / 20
self.assertLessEqual(diff2, 1 / 20)
def save(self, dlg):
"""
Stores the current CL data into a TIFF/HDF5 file
"""
f = model.ProgressiveFuture()
try:
das = self._acquire(dlg, f)
except CancelledError:
logging.debug("Stopping acquisition + export, as it was cancelled")
return
except Exception as e:
logging.exception("Failed to acquire CL data: %s", e)
return
fn = self.filename.value
bn, ext = splitext(fn)
if ext == ".png":
logging.debug("Using HDF5 instead of PNG")
fn = bn + ".h5"
exporter = dataio.find_fittest_converter(fn)
try:
exporter.export(fn, das)
except Exception:
logging.exception("Failed to store data in %s", fn)
f.set_result(None) # Indicate it's over
self._update_filename()
def test_real_perfect_overlap(self):
"""
Test on decomposed image
"""
numTiles = [2, 3, 4]
overlap = [0.4]
for img in IMGS:
conv = find_fittest_converter(img)
data = conv.read_data(img)[0]
img = ensure2DImage(data)
for n in numTiles:
for o in overlap:
[tiles, _] = decompose_image(img, o, n, "horizontalZigzag",
False)
weaver = CollageWeaverReverse()
for t in tiles:
weaver.addTile(t)
sz = len(weaver.getFullImage())
w = weaver.getFullImage()
numpy.testing.assert_allclose(w, img[:sz, :sz], rtol=1)
def test_shift_real_manual(self):
""" Test case not generated by decompose.py file and manually cropped """
for img in IMGS:
conv = find_fittest_converter(img)
data = conv.read_data(img)[0]
img = ensure2DImage(data)
cropped1 = img[0:400, 0:400]
cropped2 = img[4:404, 322:722]
registrar = GlobalShiftRegistrar()
tile1 = model.DataArray(numpy.array(cropped1), {
model.MD_PIXEL_SIZE: [1 / 20, 1 / 20], # m/px
model.MD_POS: (200 / 20, img.shape[1] / 20 - 200 / 20), # m
})
tile2 = model.DataArray(numpy.array(cropped2), {
model.MD_PIXEL_SIZE: [1 / 20, 1 / 20], # m/px
model.MD_POS: (520 / 20, img.shape[1] / 20 - 200 / 20), # m
})
registrar.addTile(tile1)
registrar.addTile(tile2)
calculatedPositions = registrar.getPositions()[0]
diff1 = calculatedPositions[1][0] - 522 / 20
self.assertLessEqual(diff1, 1 / 20)
diff2 = calculatedPositions[1][1] - img.shape[1] / 20 - 204 / 20
self.assertLessEqual(diff2, 1 / 20)
def open_acq(fn):
"""
Read the content of an acquisition file
return (list of DataArray, list of DataArray):
list of the data in the file
thumbnail (if available, might be empty)
"""
fmt_mng = dataio.find_fittest_converter(fn, default=None, mode=os.O_RDONLY)
if fmt_mng is None:
logging.warning("Failed to find a fitting importer for file %s", fn)
# TODO: try all the formats?
fmt_mng = dataio.hdf5
if not hasattr(fmt_mng, "read_data"):
raise NotImplementedError("No support for importing format %s" % fmt_mng.FORMAT)
try:
data = fmt_mng.read_data(fn)
except Exception:
raise ValueError("Failed to open the file '%s' as %s" % (fn, fmt_mng.FORMAT))
if not data:
logging.warning("Couldn't load any data from file '%s' as %s",
fn, fmt_mng.FORMAT)
try:
thumb = fmt_mng.read_thumbnail(fn)
except Exception:
logging.exception("Failed to read the thumbnail of file '%s' as %s",
fn, fmt_mng.FORMAT)
# doesn't matter that much
thumb = []
return data, thumb
def test_shift_real(self):
""" Test on decomposed image with known shift """
numTiles = [2, 3, 4]
overlap = [0.5, 0.4, 0.3, 0.2]
acq = ["horizontalLines", "horizontalZigzag", "verticalLines"]
for img, num, o, a in itertools.product(IMGS, numTiles, overlap, acq):
_, img_name = os.path.split(img)
conv = find_fittest_converter(img)
data = conv.read_data(img)[0]
data = ensure2DImage(data)
# Create artificial tiled image
[tiles, real_pos] = decompose_image(data, o, num, a)
px_size = tiles[0].metadata[model.MD_PIXEL_SIZE]
registrar = GlobalShiftRegistrar()
# Register tiles
for tile in tiles:
registrar.addTile(tile)
# Compare positions to real positions, allow 5 px offset
registered_pos = registrar.getPositions()[0]
diff = numpy.absolute(numpy.subtract(registered_pos, real_pos))
allowed_px_offset = numpy.repeat(numpy.multiply(px_size, 5), len(diff))
numpy.testing.assert_array_less(diff.flatten(), allowed_px_offset.flatten(),
"Position %s pxs off for image '%s', " % (max(diff.flatten()) / px_size[0], img_name) +
"%s x %s tiles, %s ovlp, %s method." % (num, num, o, a))
def test_shift_real(self):
""" Test on decomposed image with known shift """
numTiles = [2, 3]
overlap = [0.2, 0.3, 0.4]
acq = ["horizontalLines", "verticalLines", "horizontalZigzag"]
for img in IMGS:
conv = find_fittest_converter(img)
data = conv.read_data(img)[0]
data = ensure2DImage(data)
for num in numTiles:
for o in overlap:
for a in acq:
[tiles, pos] = decompose_image(data, o, num, a, False)
registrar = IdentityRegistrar()
for i in range(len(pos)):
registrar.addTile(tiles[i])
calculatedPositions = registrar.getPositions()[0]
diff1 = abs(calculatedPositions[i][0] - pos[i][0])
diff2 = abs(calculatedPositions[i][1] - pos[i][1])
# allow difference of 10% of overlap
px_size = tiles[i].metadata[model.MD_PIXEL_SIZE]
# allow error of 1% of tileSize
margin1 = 0.01 * tiles[i].shape[0] * px_size[0]
margin2 = 0.01 * tiles[i].shape[1] * px_size[1]
self.assertLessEqual(diff1, margin1,
"Failed for %s tiles, %s overlap and %s method," % (num, o, a) +
" %f != %f" % (calculatedPositions[i][0], pos[i][0]))
self.assertLessEqual(diff2, margin2,
"Failed for %s tiles, %s overlap and %s method," % (num, o, a) +
" %f != %f" % (calculatedPositions[i][1], pos[i][1]))
def test_no_seam(self):
"""
Test on decomposed image
"""
numTiles = [2, 3, 4]
overlap = [0.4]
for img in IMGS:
conv = find_fittest_converter(img)
data = conv.read_data(img)[0]
img = ensure2DImage(data)
for n in numTiles:
for o in overlap:
[tiles, _] = decompose_image(img, o, n, "horizontalZigzag",
False)
weaver = CollageWeaver(adjust_brightness=False)
for t in tiles:
weaver.addTile(t)
sz = len(weaver.getFullImage())
w = weaver.getFullImage()
numpy.testing.assert_array_almost_equal(w,
img[:sz, :sz],
decimal=1)
def acquire(comp_name, dataflow_names, filename):
"""
Acquire an image from one (or more) dataflow
comp_name (string): name of the detector to find
dataflow_names (list of string): name of each dataflow to access
filename (unicode): name of the output file (format depends on the extension)
"""
component = get_detector(comp_name)
# check the dataflow exists
dataflows = []
for df_name in dataflow_names:
try:
df = getattr(component, df_name)
except AttributeError:
raise ValueError("Failed to find data-flow '%s' on component %s" %
(df_name, comp_name))
if not isinstance(df, model.DataFlowBase):
raise ValueError("%s.%s is not a data-flow" % (comp_name, df_name))
dataflows.append(df)
images = []
for df in dataflows:
try:
# Note: currently, get() uses Pyro, which is not as memory efficient
# as .subscribe(), which uses ZMQ. So would need to use
# _get_big_image() if very large image is requested.
image = df.get()
except Exception as exc:
raise IOError("Failed to acquire image from component %s: %s" %
(comp_name, exc))
logging.info("Acquired an image of dimension %r.", image.shape)
images.append(image)
try:
if model.MD_PIXEL_SIZE in image.metadata:
pxs = image.metadata[model.MD_PIXEL_SIZE]
dim = (image.shape[0] * pxs[0], image.shape[1] * pxs[1])
logging.info("Physical dimension of image is %s.",
units.readable_str(dim, unit="m", sig=3))
else:
logging.warning("Physical dimension of image is unknown.")
if model.MD_SENSOR_PIXEL_SIZE in image.metadata:
spxs = image.metadata[model.MD_SENSOR_PIXEL_SIZE]
dim_sens = (image.shape[0] * spxs[0], image.shape[1] * spxs[1])
logging.info("Physical dimension of sensor is %s.",
units.readable_str(dim_sens, unit="m", sig=3))
except Exception as exc:
logging.exception("Failed to read image information.")
exporter = dataio.find_fittest_converter(filename)
try:
exporter.export(filename, images)
except IOError as exc:
raise IOError(u"Failed to save to '%s': %s" % (filename, exc))
def acquire(self, dlg):
main_data = self.main_app.main_data
str_ctrl = main_data.tab.value.streambar_controller
stream_paused = str_ctrl.pauseStreams()
dlg.pauseSettings()
nb = self.numberOfAcquisitions.value
p = self.period.value
ss, last_ss = self._get_acq_streams()
fn = self.filename.value
exporter = dataio.find_fittest_converter(fn)
bs, ext = splitext(fn)
fn_pat = bs + "-%.5d" + ext
sacqt = acq.estimateTime(ss)
intp = max(0, p - sacqt)
if p < sacqt:
logging.warning(
"Acquisition will take %g s, but period between acquisition must be only %g s",
sacqt, p
)
# TODO: if drift correction, use it over all the time
f = model.ProgressiveFuture()
f.task_canceller = lambda l: True # To allow cancelling while it's running
f.set_running_or_notify_cancel() # Indicate the work is starting now
dlg.showProgress(f)
for i in range(nb):
left = nb - i
dur = sacqt * left + intp * (left - 1)
if left == 1 and last_ss:
ss += last_ss
dur += acq.estimateTime(ss) - sacqt
startt = time.time()
f.set_progress(end=startt + dur)
das, e = acq.acquire(ss).result()
if f.cancelled():
dlg.resumeSettings()
return
exporter.export(fn_pat % (i,), das)
# Wait the period requested, excepted the last time
if left > 1:
sleept = (startt + p) - time.time()
if sleept > 0:
time.sleep(sleept)
else:
logging.info("Immediately starting next acquisition, %g s late", -sleept)
f.set_result(None) # Indicate it's over
# self.showAcquisition(self.filename.value)
dlg.Close()
def testRename(self):
"""
Check it's at least possible to open one DataArray, when the files are
renamed
"""
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
self.no_of_images = 2
metadata = [{
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_EXP_TIME: 0.2, # s
},
{
model.MD_EXP_TIME: 1.2, # s
},
]
# Add metadata
for i in range(self.no_of_images):
a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata[i])
a[white[::-1]] = 124 + i
ldata.append(a)
# export
orig_name = "boo.0.tiff"
stiff.export(orig_name, ldata)
tokens = orig_name.split(".0.", 1)
ntokens = FILENAME.split(".0.", 1)
# Renaming the file
for i in range(self.no_of_images):
fname = tokens[0] + "." + str(i) + "." + tokens[1]
new_fname = ntokens[0] + "." + str(i) + "." + ntokens[1]
os.rename(fname, new_fname)
# Iterate through the new files
for i in range(self.no_of_images):
fname = ntokens[0] + "." + str(i) + "." + ntokens[1]
fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY)
self.assertEqual(fmt_mng.FORMAT, "TIFF",
"For '%s', expected format TIFF but got %s" % (fname, fmt_mng.FORMAT))
rdata = fmt_mng.read_data(fname)
# Assert that at least one DA is there
# In practice, currently, we expected precisely 1
self.assertGreaterEqual(len(rdata), 1)
# Check the correct metadata is present
for j in range(self.no_of_images):
self.assertAlmostEqual(rdata[j].metadata[model.MD_EXP_TIME],
ldata[j].metadata[model.MD_EXP_TIME])
rthumbnail = fmt_mng.read_thumbnail(fname)
# No thumbnail handling for now, so assert that is empty
self.assertEqual(rthumbnail, [])
def on_acquisition_done(self, future):
""" Callback called when the acquisition is finished (either successfully or cancelled) """
if self._main_data_model.opm:
self._main_data_model.opm.setAcqQuality(path.ACQ_QUALITY_FAST)
# bind button back to direct closure
self.btn_cancel.Bind(wx.EVT_BUTTON, self.on_close)
self._resume_settings()
self.acquiring = False
self.acq_future = None # To avoid holding the ref in memory
self._acq_future_connector = None
try:
data = future.result(1) # timeout is just for safety
self.conf.fn_count = update_counter(self.conf.fn_count)
except CancelledError:
# put back to original state:
# re-enable the acquire button
self.btn_secom_acquire.Enable()
# hide progress bar (+ put pack estimated time)
self.update_acquisition_time()
self.gauge_acq.Hide()
self.Layout()
return
except Exception:
# We cannot do much: just warn the user and pretend it was cancelled
logging.exception("Acquisition failed")
self.btn_secom_acquire.Enable()
self.lbl_acqestimate.SetLabel("Acquisition failed.")
self.lbl_acqestimate.Parent.Layout()
# leave the gauge, to give a hint on what went wrong.
return
# Now store the data (as pyramidal data), and open it again (but now it's
# backed with the persistent storage.
try:
exporter = dataio.find_fittest_converter(self.filename)
if exporter.CAN_SAVE_PYRAMID:
exporter.export(self.filename, data, pyramid=True)
else:
logging.warning(
"File format doesn't support saving image in pyramidal form"
)
exporter.export(self.filename)
self.data = exporter.open_data(self.filename).content
except Exception:
# We cannot do much: just warn the user and pretend it was cancelled
logging.exception("Storage failed")
self.btn_secom_acquire.Enable()
self.lbl_acqestimate.SetLabel("Storage failed.")
self.lbl_acqestimate.Parent.Layout()
return
self.terminate_listeners()
self.EndModal(wx.ID_OPEN)
def get_data(fn):
reader = dataio.find_fittest_converter(fn)
das = reader.read_data(fn)
if len(das) == 0:
raise LookupError("File %s has no data" % (fn,))
elif len(das) > 1:
logging.warning("File %s has more than one data, will only use the first one", fn)
return das[0]
def get_data(fn):
reader = dataio.find_fittest_converter(fn)
das = reader.read_data(fn)
if len(das) == 0:
raise LookupError("File %s has no data" % (fn,))
elif len(das) > 1:
logging.warning("File %s has more than one data, will only use the first one", fn)
return das[0]
def acquire(comp_name, dataflow_names, filename):
"""
Acquire an image from one (or more) dataflow
comp_name (string): name of the detector to find
dataflow_names (list of string): name of each dataflow to access
filename (unicode): name of the output file (format depends on the extension)
"""
component = get_detector(comp_name)
# check the dataflow exists
dataflows = []
for df_name in dataflow_names:
try:
df = getattr(component, df_name)
except AttributeError:
raise ValueError("Failed to find data-flow '%s' on component %s" % (df_name, comp_name))
if not isinstance(df, model.DataFlowBase):
raise ValueError("%s.%s is not a data-flow" % (comp_name, df_name))
dataflows.append(df)
images = []
for df in dataflows:
try:
# Note: currently, get() uses Pyro, which is not as memory efficient
# as .subscribe(), which uses ZMQ. So would need to use
# _get_big_image() if very large image is requested.
image = df.get()
except Exception as exc:
raise IOError("Failed to acquire image from component %s: %s" % (comp_name, exc))
logging.info("Acquired an image of dimension %r.", image.shape)
images.append(image)
try:
if model.MD_PIXEL_SIZE in image.metadata:
pxs = image.metadata[model.MD_PIXEL_SIZE]
dim = (image.shape[0] * pxs[0], image.shape[1] * pxs[1])
logging.info("Physical dimension of image is %s.",
units.readable_str(dim, unit="m", sig=3))
else:
logging.warning("Physical dimension of image is unknown.")
if model.MD_SENSOR_PIXEL_SIZE in image.metadata:
spxs = image.metadata[model.MD_SENSOR_PIXEL_SIZE]
dim_sens = (image.shape[0] * spxs[0], image.shape[1] * spxs[1])
logging.info("Physical dimension of sensor is %s.",
units.readable_str(dim_sens, unit="m", sig=3))
except Exception as exc:
logging.exception("Failed to read image information.")
exporter = dataio.find_fittest_converter(filename)
try:
exporter.export(filename, images)
except IOError as exc:
raise IOError(u"Failed to save to '%s': %s" % (filename, exc))
def stitch(infns, registration_method, weaving_method):
"""
Stitches a set of tiles.
infns: file names of tiles
method: weaving method (WEAVER_MEAN or WEAVER_COLLAGE)
returns list of data arrays containing the stitched images for every stream
"""
def leader_quality(da):
"""
Function for sorting different streams. Use largest EM stream first, then other EM streams,
then other types of streams sorted by their size.
return int: The bigger the more leadership
"""
# For now, we prefer a lot the EM images, because they are usually the
# one with the smallest FoV and the most contrast
if da.metadata[model.MD_ACQ_TYPE] == model.MD_AT_EM: # SEM stream
return numpy.prod(da.shape) # More pixel to find the overlap
else:
# A lot less likely
return numpy.prod(da.shape) / 100
da_streams = [] # for each stream, a list of DataArrays
for fn in infns:
# Read data
converter = dataio.find_fittest_converter(fn)
# TODO: use open_data/DataArrayShadow when converter support it
das = converter.read_data(fn)
logging.debug("Got %d streams from file %s", len(das), fn)
# Remove the DAs we don't want to (cannot) stitch
das = add_acq_type_md(das)
das = [da for da in das if da.metadata[model.MD_ACQ_TYPE] not in \
(model.MD_AT_AR, model.MD_AT_SPECTRUM)]
# Add sorted DAs to list
das = sorted(das, key=leader_quality, reverse=True)
da_streams.append(tuple(das))
def get_acq_time(das):
return das[0].metadata.get(model.MD_ACQ_DATE, 0)
da_streams = sorted(da_streams, key=get_acq_time)
das_registered = stitching.register(da_streams, registration_method)
# Weave every stream
st_data = []
for s in range(len(das_registered[0])):
streams = []
for da in das_registered:
streams.append(da[s])
da = stitching.weave(streams, weaving_method)
da.metadata[model.MD_DIMS] = "YX"
st_data.append(da)
return st_data
def stitch(infns, registration_method, weaving_method):
"""
Stitches a set of tiles.
infns: file names of tiles
method: weaving method (WEAVER_MEAN or WEAVER_COLLAGE)
returns list of data arrays containing the stitched images for every stream
"""
def leader_quality(da):
"""
Function for sorting different streams. Use largest EM stream first, then other EM streams,
then other types of streams sorted by their size.
return int: The bigger the more leadership
"""
# For now, we prefer a lot the EM images, because they are usually the
# one with the smallest FoV and the most contrast
if da.metadata[model.MD_ACQ_TYPE] == model.MD_AT_EM: # SEM stream
return numpy.prod(da.shape) # More pixel to find the overlap
else:
# A lot less likely
return numpy.prod(da.shape) / 100
da_streams = [] # for each stream, a list of DataArrays
for fn in infns:
# Read data
converter = dataio.find_fittest_converter(fn)
# TODO: use open_data/DataArrayShadow when converter support it
das = converter.read_data(fn)
logging.debug("Got %d streams from file %s", len(das), fn)
# Remove the DAs we don't want to (cannot) stitch
das = add_acq_type_md(das)
das = [da for da in das if da.metadata[model.MD_ACQ_TYPE] not in \
(model.MD_AT_AR, model.MD_AT_SPECTRUM)]
# Add sorted DAs to list
das = sorted(das, key=leader_quality, reverse=True)
da_streams.append(tuple(das))
def get_acq_time(das):
return das[0].metadata.get(model.MD_ACQ_DATE, 0)
da_streams = sorted(da_streams, key=get_acq_time)
das_registered = stitching.register(da_streams, registration_method)
# Weave every stream
st_data = []
for s in range(len(das_registered[0])):
streams = []
for da in das_registered:
streams.append(da[s])
da = stitching.weave(streams, weaving_method)
da.metadata[model.MD_DIMS] = "YX"
st_data.append(da)
return st_data
def _fast_acquire_one(self, dlg, st, last_ss):
"""
Acquires one stream, *as fast as possible* (ie, the period is not used).
Only works with LiveStreams (and not with MDStreams)
st (LiveStream)
last_ss (list of Streams): all the streams to be acquire on the last time
"""
# Essentially, we trick a little bit the stream, by convincing it that
# we want a live view, but instead of display the data, we store them.
# It's much faster because we don't have to stop/start the detector between
# each acquisition.
nb = self.numberOfAcquisitions.value
fn = self.filename.value
self._exporter = dataio.find_fittest_converter(fn)
bs, ext = splitext(fn)
fn_pat = bs + "-%.5d" + ext
self._acq_completed = threading.Event()
f = model.ProgressiveFuture()
f.task_canceller = self._cancel_fast_acquire
f._stream = st
if last_ss:
nb -= 1
extra_dur = acq.estimateTime([st] + last_ss)
else:
extra_dur = 0
self._hijack_live_stream(st, f, nb, fn_pat, extra_dur)
try:
# Start acquisition and wait until it's done
f.set_running_or_notify_cancel(
) # Indicate the work is starting now
dlg.showProgress(f)
st.is_active.value = True
self._acq_completed.wait()
if f.cancelled():
dlg.resumeSettings()
return
finally:
st.is_active.value = False # just to be extra sure it's stopped
logging.debug("Restoring stream %s", st)
self._restore_live_stream(st)
# last "normal" acquisition, if needed
if last_ss:
logging.debug("Acquiring last acquisition, with all the streams")
ss = [st] + last_ss
f.set_progress(end=time.time() + acq.estimateTime(ss))
das, e = acq.acquire(
ss, self.main_app.main_data.settings_obs).result()
self._save_data(fn_pat % (nb, ), das)
self._stop_saving_threads() # Wait for all the data to be stored
f.set_result(None) # Indicate it's over
def __init__(self, name, role, children, image=None, drift_period=None,
daemon=None, **kwargs):
'''
children (dict string->kwargs): parameters setting for the children.
Known children are "scanner", "detector0", and the optional "focus"
They will be provided back in the .children VA
image (str or None): path to a file to use as fake image (relative to
the directory of this class)
drift_period (None or 0<float): time period for drift updating in seconds
Raise an exception if the device cannot be opened
'''
# fake image setup
if image is None:
image = u"simsem-fake-output.h5"
image = str(image)
# ensure relative path is from this file
if not os.path.isabs(image):
image = os.path.join(os.path.dirname(__file__), image)
converter = dataio.find_fittest_converter(image, mode=os.O_RDONLY)
self.fake_img = img.ensure2DImage(converter.read_data(image)[0])
self._drift_period = drift_period
# we will fill the set of children with Components later in ._children
model.HwComponent.__init__(self, name, role, daemon=daemon, **kwargs)
self._metadata[model.MD_HW_NAME] = "FakeSEM"
# create the scanner child
try:
ckwargs = children["scanner"]
except (KeyError, TypeError):
raise KeyError("SimSEM was not given a 'scanner' child")
self._scanner = Scanner(parent=self, daemon=daemon, **ckwargs)
self.children.value.add(self._scanner)
# create the detector children
self._detectors = []
for c, ckwargs in children.items():
if c.startswith("detector"):
self._detectors.append(Detector(parent=self, daemon=daemon, **ckwargs))
if not self._detectors:
raise KeyError("SimSEM was not given a 'detector0' child")
self.children.value.update(set(self._detectors))
try:
ckwargs = children["focus"]
except (KeyError, TypeError):
logging.info("Will not simulate focus")
self._focus = None
else:
self._focus = EbeamFocus(parent=self, daemon=daemon, **ckwargs)
self.children.value.add(self._focus)
def __init__(self, name, role, children, image=None, drift_period=None,
daemon=None, **kwargs):
'''
children (dict string->kwargs): parameters setting for the children.
Known children are "scanner", "detector0", and the optional "focus"
They will be provided back in the .children VA
image (str or None): path to a file to use as fake image (relative to
the directory of this class)
drift_period (None or 0<float): time period for drift updating in seconds
Raise an exception if the device cannot be opened
'''
# fake image setup
if image is None:
image = u"simsem-fake-output.h5"
image = unicode(image)
# ensure relative path is from this file
if not os.path.isabs(image):
image = os.path.join(os.path.dirname(__file__), image)
converter = dataio.find_fittest_converter(image, mode=os.O_RDONLY)
self.fake_img = img.ensure2DImage(converter.read_data(image)[0])
self._drift_period = drift_period
# we will fill the set of children with Components later in ._children
model.HwComponent.__init__(self, name, role, daemon=daemon, **kwargs)
self._metadata[model.MD_HW_NAME] = "FakeSEM"
# create the scanner child
try:
ckwargs = children["scanner"]
except (KeyError, TypeError):
raise KeyError("SimSEM was not given a 'scanner' child")
self._scanner = Scanner(parent=self, daemon=daemon, **ckwargs)
self.children.value.add(self._scanner)
# create the detector children
self._detectors = []
for c, ckwargs in children.items():
if c.startswith("detector"):
self._detectors.append(Detector(parent=self, daemon=daemon, **ckwargs))
if not self._detectors:
raise KeyError("SimSEM was not given a 'detector0' child")
self.children.value.update(set(self._detectors))
try:
ckwargs = children["focus"]
except (KeyError, TypeError):
logging.info("Will not simulate focus")
self._focus = None
else:
self._focus = EbeamFocus(parent=self, daemon=daemon, **ckwargs)
self.children.value.add(self._focus)
def testMissing(self):
"""
Check it's at least possible to open one DataArray, when the other parts
are missing.
"""
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
self.no_of_images = 2
metadata = [
{
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_EXP_TIME: 0.2, # s
},
{
model.MD_EXP_TIME: 1.2, # s
},
]
# Add metadata
for i in range(self.no_of_images):
a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata[i])
a[white[::-1]] = 124 + i
ldata.append(a)
# export
stiff.export(FILENAME, ldata)
# Ooops, the first file is gone => it should still be possible to open
# the other files
os.remove(FILENAME)
tokens = FILENAME.split(".0.", 1)
for i in range(1, self.no_of_images):
fname = tokens[0] + "." + str(i) + "." + tokens[1]
fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY)
self.assertEqual(
fmt_mng.FORMAT, "TIFF",
"For '%s', expected format TIFF but got %s" %
(fname, fmt_mng.FORMAT))
rdata = fmt_mng.read_data(fname)
# Assert that at least one DA is there
# In practice, currently, we expected precisely 1
self.assertGreaterEqual(len(rdata), 1)
# Check the correct metadata is present
self.assertAlmostEqual(rdata[0].metadata[model.MD_EXP_TIME],
ldata[i].metadata[model.MD_EXP_TIME])
rthumbnail = fmt_mng.read_thumbnail(fname)
# No thumbnail handling for now, so assert that is empty
self.assertEqual(rthumbnail, [])
def save_acq(fn, data, thumbs):
"""
Saves to a file the data and thumbnail
"""
exporter = dataio.find_fittest_converter(fn)
# For now the exporter supports only one thumbnail
if thumbs:
thumb = thumbs[0]
else:
thumb = None
exporter.export(fn, data, thumb)
def testMissing(self):
"""
Check it's at least possible to open one DataArray, when the other parts
are missing.
"""
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
self.no_of_images = 2
metadata = [{
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_EXP_TIME: 0.2, # s
},
{
model.MD_EXP_TIME: 1.2, # s
},
]
# Add metadata
for i in range(self.no_of_images):
a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata[i])
a[white[::-1]] = 124 + i
ldata.append(a)
# export
stiff.export(FILENAME, ldata)
# Ooops, the first file is gone => it should still be possible to open
# the other files
os.remove(FILENAME)
tokens = FILENAME.split(".0.", 1)
for i in range(1, self.no_of_images):
fname = tokens[0] + "." + str(i) + "." + tokens[1]
fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY)
self.assertEqual(fmt_mng.FORMAT, "TIFF",
"For '%s', expected format TIFF but got %s" % (fname, fmt_mng.FORMAT))
rdata = fmt_mng.read_data(fname)
# Assert that at least one DA is there
# In practice, currently, we expected precisely 1
self.assertGreaterEqual(len(rdata), 1)
# Check the correct metadata is present
self.assertAlmostEqual(rdata[0].metadata[model.MD_EXP_TIME],
ldata[i].metadata[model.MD_EXP_TIME])
rthumbnail = fmt_mng.read_thumbnail(fname)
# No thumbnail handling for now, so assert that is empty
self.assertEqual(rthumbnail, [])
def save_data(das, filename):
"""
Saves a series of spectra
das (list of DataArray): data to save
filename (str)
"""
exporter = dataio.find_fittest_converter(filename)
if os.path.exists(filename):
# mostly to warn if multiple ypos/xpos are rounded to the same value
logging.warning("Overwriting file '%s'.", filename)
else:
logging.info("Saving file '%s", filename)
exporter.export(filename, das)
def save_data(das, filename):
"""
Saves a series of spectra
das (list of DataArray): data to save
filename (str)
"""
exporter = dataio.find_fittest_converter(filename)
if os.path.exists(filename):
# mostly to warn if multiple ypos/xpos are rounded to the same value
logging.warning("Overwriting file '%s'.", filename)
else:
logging.info("Saving file '%s", filename)
exporter.export(filename, das)
def save_data(self, data, fn):
"""
Saves the data into a file
data (model.DataArray or list of model.DataArray): the data to save
fn (unicode): filename of the file to save
"""
exporter = dataio.find_fittest_converter(fn)
if os.path.exists(fn):
# mostly to warn if multiple ypos/xpos are rounded to the same value
logging.warning("Overwriting file '%s'.", fn)
else:
logging.info("Saving file '%s'", fn)
exporter.export(unicode(fn), data)
def acquire_timelapse(num, filename):
ccd = None
# find components by their role
for c in model.getComponents():
if c.role == "ccd":
ccd = c
images = []
for i in range(num):
logging.info("Acquiring image %d", i + 1)
images.append(ccd.data.get())
# save the file
exporter = dataio.find_fittest_converter(filename)
exporter.export(filename, images)
def acquire_timelapse(num, filename):
ccd = None
# find components by their role
for c in model.getComponents():
if c.role == "ccd":
ccd = c
images = []
for i in range(num):
logging.info("Acquiring image %d", i + 1)
images.append(ccd.data.get())
# save the file
exporter = dataio.find_fittest_converter(filename)
exporter.export(filename, images)
def testExportOpener(self):
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
self.no_of_images = 2
metadata = [
{
model.MD_IN_WL: (500e-9, 520e-9), # m
},
{
model.MD_EXP_TIME: 1.2, # s
},
]
# Add wavelength metadata just to group them
for i in range(self.no_of_images):
a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata[i])
a[white[::-1]] = 124
ldata.append(a)
# export
stiff.export(FILENAME, ldata)
tokens = FILENAME.split(".0.", 1)
# Iterate through the files generated. Opening any of them should be
# returning _all_ the DAs
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY)
self.assertEqual(
fmt_mng.FORMAT, "TIFF",
"For '%s', expected format TIFF but got %s" %
(fname, fmt_mng.FORMAT))
rdata = fmt_mng.read_data(fname)
# Assert all the DAs are there
self.assertEqual(len(rdata), len(ldata))
for da in rdata:
self.assertEqual(da[white[::-1]], 124)
rthumbnail = fmt_mng.read_thumbnail(fname)
# No thumbnail handling for now, so assert that is empty
self.assertEqual(rthumbnail, [])
def test_find_fittest_converter_read(self):
# input args -> format name
test_io = [(("coucou.h5",), "HDF5"),
(("coucou.le monde.hdf5",), "HDF5"),
(("some/fancy/../path/file.tiff",), "TIFF"),
(("some/fancy/../.hdf5/h5.ome.tiff",), "TIFF"),
(("a/b/d.tiff",), "TIFF"),
(("a/b/d.ome.tiff",), "TIFF"),
(("a/b/d.h5",), "HDF5"),
(("a/b/d.b",), "TIFF"), # fallback to tiff
(("d.hdf5",), "HDF5"),
(("a/b/d.0.ome.tiff",), "TIFF"), # Serialised TIFF must be opened by TIFF
]
for args, fmt_exp in test_io:
fmt_mng = find_fittest_converter(*args, mode=os.O_RDONLY)
self.assertEqual(fmt_mng.FORMAT, fmt_exp,
"For '%s', expected format %s but got %s" % (args[0], fmt_exp, fmt_mng.FORMAT))
def save_data(self, data, fn):
"""
Saves the data into a file
data (model.DataArray or list of model.DataArray): the data to save
fn (unicode): filename of the file to save
"""
exporter = dataio.find_fittest_converter(fn)
# TODO: put the first data in a StaticStream to get a thumbnail
if os.path.exists(fn):
# mostly to warn if multiple ypos/xpos are rounded to the same value
logging.warning("Overwriting file '%s'.", fn)
else:
logging.info("Saving file '%s'", fn)
exporter.export(fn, data)
def testExportOpener(self):
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
self.no_of_images = 2
metadata = [{
model.MD_IN_WL: (500e-9, 520e-9), # m
},
{
model.MD_EXP_TIME: 1.2, # s
},
]
# Add wavelength metadata just to group them
for i in range(self.no_of_images):
a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata[i])
a[white[::-1]] = 124
ldata.append(a)
# export
stiff.export(FILENAME, ldata)
tokens = FILENAME.split(".0.", 1)
# Iterate through the files generated. Opening any of them should be
# returning _all_ the DAs
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY)
self.assertEqual(fmt_mng.FORMAT, "TIFF",
"For '%s', expected format TIFF but got %s" % (fname, fmt_mng.FORMAT))
rdata = fmt_mng.read_data(fname)
# Assert all the DAs are there
self.assertEqual(len(rdata), len(ldata))
for da in rdata:
self.assertEqual(da[white[::-1]], 124)
rthumbnail = fmt_mng.read_thumbnail(fname)
# No thumbnail handling for now, so assert that is empty
self.assertEqual(rthumbnail, [])
def acquire(self, dlg):
nb = self.numberOfAcquisitions.value
p = self.period.value
sacqt = self._stream.estimateAcquisitionTime()
intp = max(0, p - sacqt)
if p < sacqt:
logging.warning(
"Acquisition will take %g s, but period between acquisition must be only %g s",
sacqt, p)
exporter = dataio.find_fittest_converter(self.filename.value)
f = model.ProgressiveFuture()
f.task_canceller = lambda l: True # To allow cancelling while it's running
f.set_running_or_notify_cancel() # Indicate the work is starting now
dlg.showProgress(f)
das = []
for i in range(nb):
left = nb - i
dur = sacqt * left + intp * (left - 1)
startt = time.time()
f.set_progress(end=startt + dur)
d, e = acq.acquire([self._stream]).result()
das.extend(d)
if f.cancelled():
return
# Wait the period requested, excepted the last time
if left > 1:
sleept = (startt + p) - time.time()
if sleept > 0:
time.sleep(sleept)
else:
logging.info(
"Immediately starting next acquisition, %g s late",
-sleept)
exporter.export(self.filename.value, das)
f.set_result(None) # Indicate it's over
# self.showAcquisition(self.filename.value)
dlg.Destroy()
def test_real_images_identity(self):
"""
Test register wrapper function
"""
for img in IMGS:
conv = find_fittest_converter(img)
data = conv.read_data(img)[0]
img = ensure2DImage(data)
num = 2
o = 0.2
a = "horizontalZigzag"
[tiles, pos] = decompose_image(img, o, num, a, False)
upd_tiles = register(tiles, method=REGISTER_IDENTITY)
for i in range(len(upd_tiles)):
calculatedPosition = upd_tiles[i].metadata[model.MD_POS]
self.assertAlmostEqual(calculatedPosition[0], pos[i][0], places=1)
self.assertAlmostEqual(calculatedPosition[1], pos[i][1], places=1)
def acquire(self, dlg):
nb = self.numberOfAcquisitions.value
p = self.period.value
sacqt = self._stream.estimateAcquisitionTime()
intp = max(0, p - sacqt)
if p < sacqt:
logging.warning(
"Acquisition will take %g s, but period between acquisition must be only %g s",
sacqt, p
)
exporter = dataio.find_fittest_converter(self.filename.value)
f = model.ProgressiveFuture()
f.task_canceller = lambda l: True # To allow cancelling while it's running
f.set_running_or_notify_cancel() # Indicate the work is starting now
dlg.showProgress(f)
das = []
for i in range(nb):
left = nb - i
dur = sacqt * left + intp * (left - 1)
startt = time.time()
f.set_progress(end=startt + dur)
d, e = acq.acquire([self._stream]).result()
das.extend(d)
if f.cancelled():
return
# Wait the period requested, excepted the last time
if left > 1:
sleept = (startt + p) - time.time()
if sleept > 0:
time.sleep(sleept)
else:
logging.info("Immediately starting next acquisition, %g s late", -sleept)
exporter.export(self.filename.value, das)
f.set_result(None) # Indicate it's over
# self.showAcquisition(self.filename.value)
dlg.Destroy()
def test_no_seam(self):
"""
Test on decomposed image
"""
for img in IMGS:
conv = find_fittest_converter(img)
data = conv.read_data(img)[0]
img = ensure2DImage(data)
numTiles = [2, 3, 4]
overlap = [0.2, 0.3, 0.4]
for n in numTiles:
for o in overlap:
[tiles, _] = decompose_image(
img, o, n, "horizontalZigzag", False)
w = weave(tiles, WEAVER_MEAN)
sz = len(w)
numpy.testing.assert_allclose(w, img[:sz, :sz], rtol=1)
def testExportOpener(self):
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
num = 2
metadata = {
model.MD_IN_WL: (500e-9, 520e-9), # m
}
for i in range(num):
a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata)
a[white[::-1]] = 124
ldata.append(a)
# export
stiff.export(FILENAME, ldata)
tokens = FILENAME.split(".0.", 1)
self.no_of_images = 1
# Iterate through the files generated
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY)
self.assertEqual(fmt_mng.FORMAT, "TIFF",
"For '%s', expected format TIFF but got %s" % (fname, fmt_mng.FORMAT))
rdata = fmt_mng.read_data(fname)
# Assert all the DAs are there
self.assertEqual(len(rdata[file_index]), len(ldata))
rthumbnail = fmt_mng.read_thumbnail(fname)
# No thumbnail handling for now, so assert that is empty
self.assertEqual(rthumbnail, [])
self.no_of_images = 1
# Iterate through the files generated
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
os.remove(fname)
def __init__(self, fn, number):
self.number = number
# get the components
self.light = model.getComponent(role="light")
self.ccd = model.getComponent(role="ccd")
# prepare the data export
self.exporter = dataio.find_fittest_converter(fn)
# Make the name "fn" -> "~/Pictures + fn + fn-XXXX.ext"
path, base = os.path.split(fn)
bn, ext = os.path.splitext(base)
tmpl = os.path.join(path, bn, bn + "-%05d." + ext)
if path.startswith("/"):
# if fn starts with / => don't add ~/Pictures
self.fntmpl = tmpl
else:
self.fntmpl = os.path.join(get_picture_folder(), tmpl)
self._n = 0