def add_acq_type_md(das):
"""
Add acquisition type to das.
returns: das with updated metadata
"""
streams = io.data_to_static_streams(das)
for da, stream in zip(das, streams):
if isinstance(stream, StaticSEMStream):
da.metadata[model.MD_ACQ_TYPE] = model.MD_AT_EM
elif isinstance(stream, StaticCLStream):
da.metadata[model.MD_ACQ_TYPE] = model.MD_AT_CL
elif isinstance(stream, StaticARStream):
da.metadata[model.MD_ACQ_TYPE] = model.MD_AT_AR
elif isinstance(stream, StaticSpectrumStream):
da.metadata[model.MD_ACQ_TYPE] = model.MD_AT_SPECTRUM
elif isinstance(stream, StaticFluoStream):
da.metadata[model.MD_ACQ_TYPE] = model.MD_AT_FLUO
else:
da.metadata[model.MD_ACQ_TYPE] = "Unknown"
logging.warning("Unexpected stream of shape %s in input data." % da.shape)
# If AR Stream is present, multiple data arrays are created. The data_to_static_streams
# function returns a single ARStream, so in this case many data arrays will not be assigned a
# stream and therefore also don't have an acquisition type.
for da in das:
if model.MD_ACQ_TYPE not in da.metadata:
da.metadata[model.MD_ACQ_TYPE] = model.MD_AT_AR
return das
def add_acq_type_md(das):
"""
Add acquisition type to das.
returns: das with updated metadata
"""
streams = io.data_to_static_streams(das)
for da, stream in zip(das, streams):
if isinstance(stream, StaticSEMStream):
da.metadata[model.MD_ACQ_TYPE] = model.MD_AT_EM
elif isinstance(stream, StaticCLStream):
da.metadata[model.MD_ACQ_TYPE] = model.MD_AT_CL
elif isinstance(stream, StaticARStream):
da.metadata[model.MD_ACQ_TYPE] = model.MD_AT_AR
elif isinstance(stream, StaticSpectrumStream):
da.metadata[model.MD_ACQ_TYPE] = model.MD_AT_SPECTRUM
elif isinstance(stream, StaticFluoStream):
da.metadata[model.MD_ACQ_TYPE] = model.MD_AT_FLUO
else:
da.metadata[model.MD_ACQ_TYPE] = "Unknown"
logging.warning("Unexpected stream of shape %s in input data." % da.shape)
# If AR Stream is present, multiple data arrays are created. The data_to_static_streams
# function returns a single ARStream, so in this case many data arrays will not be assigned a
# stream and therefore also don't have an acquisition type.
for da in das:
if model.MD_ACQ_TYPE not in da.metadata:
da.metadata[model.MD_ACQ_TYPE] = model.MD_AT_AR
return das
def on_acquisition_done(self, future, num):
"""
Callback called when the one overview image acquisition is finished.
"""
try:
da = future.result()
except CancelledError:
self._reset_acquisition_gui()
return
except Exception:
# leave the gauge, to give a hint on what went wrong.
logging.exception("Acquisition failed")
self._reset_acquisition_gui("Acquisition failed (see log panel).", level=logging.WARNING)
return
# Store DataArray as TIFF in pyramidal format and reopen as static stream (to be memory-efficient)
# TODO: pick a different name from previous acquisition?
fn = os.path.join(get_picture_folder(), "fastem_overview_%s.ome.tiff" % num)
dataio.tiff.export(fn, da, pyramid=True)
da = open_acquisition(fn)
s = data_to_static_streams(da)[0]
s = FastEMOverviewStream(s.name.value, s.raw[0])
# Dict VA needs to be explicitly copied, otherwise it doesn't detect the change
ovv_ss = self._main_data_model.overview_streams.value.copy()
ovv_ss[num] = s
self._main_data_model.overview_streams.value = ovv_ss
def export_ar_to_csv(fn, background=None):
"""
fn (str): full path to the AR data file
background (DataArray or None): background data to subtract
"""
das = dataio.open_acquisition(fn)
if not das: # No such file or file doesn't contain data
return
streams = dataio.data_to_static_streams(das)
# Remove the extension of the filename, to extend the name with .csv
fn_base = dataio.splitext(fn)[0]
ar_streams = [s for s in streams if isinstance(s, ARStream)]
for s in ar_streams:
try:
s.background.value = background
except Exception as ex:
logging.error("Failed to use background data: %s", ex)
ar_proj = stream.ARRawProjection(s)
# Export every position separately
for p in s.point.choices:
if p == (None,
None): # Special "non-selected point" => not interesting
continue
s.point.value = p
# Project to "raw" = Theta vs phi array
exdata = img.ar_to_export_data([ar_proj], raw=True)
# Pick a good name
fn_csv = fn_base
if len(ar_streams) > 1: # Add the name of the stream
fn_csv += "-" + s.name.value
if len(s.point.choices) > 2:
# More than one point in the stream => add position (in µm)
fn_csv += f"-{p[0] * 1e6}-{p[1] * 1e6}"
fn_csv += ".csv"
# Save into a CSV file
logging.info("Exporting point %s to %s", p, fn_csv)
csv.export(fn_csv, exdata)
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 test_data_to_stream_pyramidal(self):
"""
Check data_to_static_streams with pyramidal images using DataArrayShadows
"""
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: "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,
},
]
# 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, pyramid=True)
# check data
rdata = open_acquisition(FILENAME)
sts = data_to_static_streams(rdata)
# There should be 3 streams: 2 fluo + 1 SEM
fluo = sem = 0
for s in sts:
if isinstance(s, stream.StaticFluoStream):
fluo += 1
elif isinstance(s, stream.EMStream):
sem += 1
self.assertEqual(fluo, 2)
self.assertEqual(sem, 1)
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 test_data_to_stream_pyramidal(self):
"""
Check data_to_static_streams with pyramidal images using DataArrayShadows
"""
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: "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,
},
]
# 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, pyramid=True)
# check data
rdata = open_acquisition(FILENAME)
sts = data_to_static_streams(rdata)
# There should be 3 streams: 2 fluo + 1 SEM
fluo = sem = 0
for s in sts:
if isinstance(s, stream.StaticFluoStream):
fluo += 1
elif isinstance(s, stream.EMStream):
sem += 1
self.assertEqual(fluo, 2)
self.assertEqual(sem, 1)
def _on_overview_acquire(self, evt):
# Disable direct image update, as it would duplicate the overview image, but less pretty.
self.main_data.stage.position.unsubscribe(self.on_stage_pos_change)
self._on_current_stream([])
try:
das = self._acquisition_controller.open_acquisition_dialog()
finally:
self.main_data.stage.position.subscribe(self.on_stage_pos_change)
self._on_current_stream(self._data_model.streams.value)
if not das:
return
for da in das:
logging.debug("Acquired overview image %s FoV: %s",
da.metadata.get(MD_DESCRIPTION, ""),
getBoundingBox(da))
# Store the data somewhere, so that it's possible to open it full size later
self._save_overview(das)
# Convert each DataArray to a Stream + Projection, so that we can display it
streams = udataio.data_to_static_streams(das)
# Only reset the channels which have a new data
opt = [s for s in streams if isinstance(s, stream.OpticalStream)]
if opt:
self._bkg_opt[:] = 0
em = [s for s in streams if isinstance(s, stream.EMStream)]
if em:
self._bkg_sem[:] = 0
# Compute the projection, this is done asynchronously (and for now,
# all at the same time, which might be clever... or not, if the
# data is really large and the memory is limited)
projs = [stream.RGBSpatialProjection(s) for s in opt + em]
logging.debug("Adding %s streams to the overview", len(projs))
for p in projs:
def add_bkg_ovv(im, proj=p):
"""
Receive the projected image (RGB) and add it to the overview
"""
# To handle cases where the projection was faster than subscribing,
# we get called at subscription. If we receive None, we just need
# to be a little bit more patient.
if im is None:
return
if isinstance(proj.stream, stream.OpticalStream):
bkg = self._bkg_opt
else:
bkg = self._bkg_sem
insert_tile_to_image(im, bkg)
logging.debug("Added overview projection %s", proj.name.value)
# Normally not necessary as the image will not change, and the
# projection + stream will go out of scope, which will cause
# the VA to be unsubscribed automatically. But it feels cleaner.
proj.image.unsubscribe(add_bkg_ovv)
del self._bkg_ovv_subs[proj]
# We could only do it when _bkg_ovv_subs is empty, as a sign it's
# the last one... but it could delay quite a bit, and could easily
# break if for some reason projection fails.
self._update_ovv()
# Keep a reference
self._bkg_ovv_subs[p] = add_bkg_ovv
p.image.subscribe(add_bkg_ovv, init=True)