def _set_fan(self, enable):
"""
Turn on/off the fan of the CCD
enable (boolean): True to turn on/restore the fan, and False to turn if off
"""
if not model.hasVA(self._main_data_model.ccd, "fanSpeed"):
return
fs = self._main_data_model.ccd.fanSpeed
if enable:
if self._orig_fan_speed is not None:
fs.value = max(fs.value, self._orig_fan_speed)
else:
self._orig_fan_speed = fs.value
fs.value = 0
# Raise targetTemperature to max/ambient to avoid the fan from
# automatically starting again. (Some hardware have this built-in when
# the current temperature is too high compared to the target)
if model.hasVA(self._main_data_model.ccd, "targetTemperature"):
temp = self._main_data_model.ccd.targetTemperature
if enable:
if self._orig_fan_temp is not None:
temp.value = min(temp.value, self._orig_fan_temp)
else:
self._orig_fan_temp = temp.value
# TODO: handle choices
try:
temp.value = min(temp.range[1],
25) # don't set above ambient temperature
except Exception:
logging.warning(
"Failed to change targetTemperature when disabling fan",
exc_info=True)
def AutoFocus(detector, emt, focus, dfbkg=None, good_focus=None, rng_focus=None, method='binary'):
"""
Wrapper for DoAutoFocus. It provides the ability to check the progress of autofocus
procedure or even cancel it.
detector (model.DigitalCamera or model.Detector): Detector on which to
improve the focus quality
emt (None or model.Emitter): In case of a SED this is the scanner used
focus (model.Actuator): The focus actuator
dfbkg (model.DataFlow or None): If provided, will be used to start/stop
the e-beam emission (it must be the dataflow of se- or bs-detector) in
order to do background subtraction. If None, no background subtraction is
performed.
good_focus (float): if provided, an already known good focus position to be
taken into consideration while autofocusing
rng_focus (tuple): if provided, the search of the best focus position is limited
within this range
method (str): focusing method, if 'binary' we follow a binary method while in
case of 'exhaustive' we iterate through the whole provided range
returns (model.ProgressiveFuture): Progress of DoAutoFocus, whose result() will return:
Focus position (m)
Focus level
"""
# Create ProgressiveFuture and update its state to RUNNING
est_start = time.time() + 0.1
# Check if the emitter is a scanner (focusing = SEM)
if model.hasVA(emt, "dwellTime"):
et = emt.dwellTime.value * numpy.prod(emt.resolution.value)
elif model.hasVA(detector, "exposureTime"):
et = detector.exposureTime.value
else:
# Completely random... but we are in a case where probably that's the last
# thing the caller will care about.
et = 1
f = model.ProgressiveFuture(start=est_start,
end=est_start + estimateAutoFocusTime(et))
f._autofocus_state = RUNNING
f._autofocus_lock = threading.Lock()
f.task_canceller = _CancelAutoFocus
# Run in separate thread
if method == "exhaustive":
autofocus_fn = _DoExhaustiveFocus
elif method == "binary":
autofocus_fn = _DoBinaryFocus
else:
raise ValueError("Unknown autofocus method")
autofocus_thread = threading.Thread(target=executeTask,
name="Autofocus",
args=(f, autofocus_fn, f, detector, emt,
focus, dfbkg, good_focus, rng_focus))
autofocus_thread.start()
return f
def __init__(self, name, role, children, backlash, **kwargs):
"""
children (dict str -> Stage): dict containing one component, the stage
to wrap
backlash (dict str -> float): for each axis of the stage, the additional
distance to move (and the direction). If an axis of the stage is not
present, then it’s the same as having 0 as backlash (=> no antibacklash
motion is performed for this axis)
"""
if len(children) != 1:
raise ValueError("AntiBacklashActuator needs 1 child")
for a, v in backlash.items():
if not isinstance(a, basestring):
raise ValueError("Backlash key must be a string but got '%s'" % (a,))
if not isinstance(v, numbers.Real):
raise ValueError("Backlash value of %s must be a number but got '%s'" % (a, v))
self._child = children.values()[0]
self._backlash = backlash
axes_def = {}
for an, ax in self._child.axes.items():
axes_def[an] = copy.deepcopy(ax)
axes_def[an].canUpdate = True
# Whether currently a backlash shift is applied on an axis
# If True, moving the axis by the backlash value would restore its expected position
# _shifted_lock must be taken before modifying this attribute
self._shifted = dict((a, False) for a in axes_def.keys())
self._shifted_lock = threading.Lock()
# look for axes in backlash not existing in the child
missing = set(backlash.keys()) - set(axes_def.keys())
if missing:
raise ValueError("Child actuator doesn't have the axes %s" % (missing,))
model.Actuator.__init__(self, name, role, axes=axes_def,
children=children, **kwargs)
# will take care of executing axis moves asynchronously
self._executor = CancellableThreadPoolExecutor(max_workers=1) # one task at a time
# Duplicate VAs which are just identical
# TODO: shall we "hide" the antibacklash move by not updating position
# while doing this move?
self.position = self._child.position
if model.hasVA(self._child, "referenced"):
self.referenced = self._child.referenced
if model.hasVA(self._child, "speed"):
self.speed = self._child.speed
def __init__(self, name, role, dependencies, **kwargs):
"""
dependencies (dict str->Component): the two components to wrap together.
The key must be "light" for the emitter component, and "clock" for the clock generator.
"""
# This will create the .powerSupply VA
model.Emitter.__init__(self,
name,
role,
dependencies=dependencies,
**kwargs)
self._shape = ()
# Determine child objects. Light
try:
self._light = dependencies["light"]
except KeyError:
raise ValueError("No 'light' child provided")
if not isinstance(self._light, model.ComponentBase):
raise ValueError("Child %s is not an emitter." %
(self._light.name, ))
if not model.hasVA(self._light, 'power'):
raise ValueError("Child %s has no power VA." %
(self._light.name, ))
if not model.hasVA(self._light, 'emissions'):
raise ValueError("Child %s has no emissions VA." %
(self._light.name, ))
# Clock generator
try:
self._clock = dependencies["clock"]
except KeyError:
raise ValueError("No 'clock generator' child provided")
if not isinstance(self._clock, model.ComponentBase):
raise ValueError("Child %s is not a Component." %
(self._clock.name, ))
if not model.hasVA(self._clock, "period"):
raise ValueError("Child %s has no period VA." %
(self._clock.name, ))
# Only one VA from the clock
self.period = self._clock.period
# All the other VAs are straight from the light
self.emissions = self._light.emissions
self.spectra = self._light.spectra
self.power = self._light.power
# Turn off/on the power of the clock based on the light power
self.emissions.subscribe(self._onEmissions)
self.power.subscribe(self._onPower)
def addst(self):
main_data = self.main_app.main_data
stctrl = self._tab.streambar_controller
axes = stctrl._filter_axes(
{"filter": ("band", main_data.light_filter)})
# TODO: special live stream?
ar_stream = ARSettingsStream(
"CL intensity on CCD",
main_data.ccd,
main_data.ccd.data,
main_data.ebeam,
sstage=main_data.scan_stage,
opm=main_data.opm,
axis_map=axes,
detvas=get_local_vas(main_data.ccd, main_data.hw_settings_config),
)
# TODO: Allow very large binning on the CCD
# Make sure the binning is not crazy (especially can happen if CCD is shared for spectrometry)
if model.hasVA(ar_stream, "detBinning"):
b = ar_stream.detBinning.value
if b[0] != b[1] or b[0] > 16:
ar_stream.detBinning.value = ar_stream.detBinning.clip((1, 1))
ar_stream.detResolution.value = ar_stream.detResolution.range[
1]
# Create the equivalent MDStream
sem_stream = self._tab.tab_data_model.semStream
sem_cl_stream = SEMCLCCDStream("SEM CLi CCD", [sem_stream, ar_stream])
return stctrl._addRepStream(ar_stream, sem_cl_stream)
def test_binning(self):
if (not model.hasVA(self.camera, "binning") or
self.camera.binning.readonly):
self.skipTest("Camera doesn't support setting binning")
self.camera.binning.value = (1, 1)
max_binning = self.camera.binning.range[1]
new_binning = (2, 2)
if new_binning >= max_binning:
# if there is no binning 2, let's not try
self.skipTest("Camera doesn't support binning")
# binning should automatically resize the image
prev_size = self.camera.resolution.value
self.camera.binning.value = new_binning
self.assertNotEqual(self.camera.resolution.value, prev_size)
# ask for the whole image
self.size = (self.camera.shape[0] // 2, self.camera.shape[1] // 2)
self.camera.resolution.value = self.size
exposure = 0.1
self.camera.exposureTime.value = exposure
start = time.time()
im = self.camera.data.get()
duration = time.time() - start
self.assertEqual(im.shape, self.size[::-1]) # TODO a small size diff is fine if bigger than requested
self.assertGreaterEqual(duration, exposure, "Error execution took %f s, less than exposure time %f." % (duration, exposure))
self.assertIn(model.MD_EXP_TIME, im.metadata)
self.assertEqual(im.metadata[model.MD_BINNING], new_binning)
def addst(self):
main_data = self.main_app.main_data
# TODO: special live stream?
ar_stream = ARSettingsStream(
"CL intensity on CCD",
main_data.ccd,
main_data.ccd.data,
main_data.ebeam,
sstage=main_data.scan_stage,
opm=main_data.opm,
detvas=get_local_vas(main_data.ccd, main_data.hw_settings_config),
)
# TODO: Allow very large binning on the CCD
# Make sure the binning is not crazy (especially can happen if CCD is shared for spectrometry)
if model.hasVA(ar_stream, "detBinning"):
b = ar_stream.detBinning.value
if b[0] != b[1] or b[0] > 16:
ar_stream.detBinning.value = ar_stream.detBinning.clip((1, 1))
ar_stream.detResolution.value = ar_stream.detResolution.range[1]
# Create the equivalent MDStream
sem_stream = self._tab.tab_data_model.semStream
sem_cl_stream = SEMCLCCDStream("SEM CLi CCD", [sem_stream, ar_stream])
stctrl = self._tab.streambar_controller
return stctrl._addRepStream(ar_stream, sem_cl_stream,
axes={"band": main_data.light_filter}
)
def test_change_settings(self):
"""
Start an acquisition, and stop just before it should end, then change the
settings, and acquire again. Check that we don't get the original acquisition
with the old settings.
(Such bug happened on the Andorcam2)
"""
if not model.hasVA(self.camera,
"binning") or self.camera.binning.readonly:
self.skipTest("Camera doesn't support setting binning")
self.camera.binning.value = self.camera.binning.clip((2, 2))
self.size = self.camera.resolution.value
self.camera.exposureTime.value = 0.09
self.left = 1
self.camera.data.subscribe(self.receive_image)
time.sleep(0.05)
logging.debug("Stopping acquisition")
self.camera.data.unsubscribe(self.receive_image)
# Never received an image...
self.assertEqual(self.left, 1)
# Change settings
logging.debug("Changing binning")
self.camera.binning.value = (1, 1)
exp_res = self.camera.resolution.value[::-1]
da = self.camera.data.get()
logging.debug("Got res of %s", da.shape)
self.assertEqual(da.shape, exp_res)
def _updateHistogram(self, data=None):
if data is None and model.hasVA(self, "zIndex"):
data = self.raw[0]
dims = data.metadata.get(model.MD_DIMS, "CTZYX"[-data.ndim::])
if dims == "ZYX" and data.ndim == 3:
data = img.getYXFromZYX(data, self.zIndex.value) # Remove extra dimensions (of length 1)
super(Static2DStream, self)._updateHistogram(data)
def _moveToTile(self, idx, prev_idx, tile_size):
"""
Move the stage to the tile position
:param idx: (tuple (float, float)) current index of tile
:param prev_idx: (tuple (float, float)) previous index of tile
:param tile_size: (tuple (float, float)) total tile size
"""
overlap = 1 - self._overlap
# don't move on the axis that is not supposed to have changed
m = {}
idx_change = numpy.subtract(idx, prev_idx)
if idx_change[0]:
m["x"] = self._starting_pos["x"] + idx[0] * tile_size[0] * overlap
if idx_change[1]:
m["y"] = self._starting_pos["y"] - idx[1] * tile_size[1] * overlap
logging.debug("Moving to tile %s at %s m", idx, m)
f = self._stage.moveAbs(m)
try:
speed = min(self._stage.speed.value.values()) if model.hasVA(
self._stage, "speed") else 10e-6
# add 1 to make sure it doesn't time out in case of a very small move
t = math.hypot(
abs(idx_change[0]) * tile_size[0] * overlap,
abs(idx_change[1]) * tile_size[1] * overlap) / speed + 1
f.result(t)
except TimeoutError:
logging.warning("Failed to move to tile %s", idx)
self._future.running_subf.cancel()
def _updateHistogram(self, data=None):
if data is None and model.hasVA(self, "zIndex"):
data = self.raw[0]
dims = data.metadata.get(model.MD_DIMS, "CTZYX"[-data.ndim::])
if dims == "ZYX" and data.ndim == 3:
data = img.getYXFromZYX(data, self.zIndex.value) # Remove extra dimensions (of length 1)
super(Static2DStream, self)._updateHistogram(data)
def start(self):
"""
Called when the menu entry is selected
"""
main_data = self.main_app.main_data
# Stop the streams
tab_data = main_data.tab.value.tab_data_model
for s in tab_data.streams.value:
s.should_update.value = False
self.filename.value = self._get_new_filename()
self._update_exp_dur()
# Special CCD settings to get values as photon counting
if model.hasVA(self.ccd, "countConvert"):
self.ccd.countConvert.value = 2 # photons
dlg = AcquisitionDialog(
self, "Super-resolution acquisition",
"Acquires a series of shortly exposed images, "
"and store them in sequence.\n"
"Note, the advanced settings are only applied "
"after restarting the stream.")
dlg.addStream(self._stream)
dlg.addSettings(self, self.vaconf)
dlg.addButton("Close")
dlg.addButton("Acquire", self.acquire, face_colour='blue')
dlg.Maximize()
ans = dlg.ShowModal()
# Make sure the stream is not playing anymore and CCD is back to normal
self._stream.should_update.value = False
if model.hasVA(self.ccd, "countConvert"):
try:
self.ccd.countConvert.value = 0 # normal
except Exception:
logging.exception("Failed to set back count convert mode")
if ans == 0:
logging.info("Acquisition cancelled")
elif ans == 1:
logging.info("Acquisition completed")
else:
logging.warning("Got unknown return code %s", ans)
dlg.Destroy()
def addSpectrum(self, name, detector):
"""
name (str): name of the stream
detector (DigitalCamera): spectrometer to acquire the spectrum
"""
logging.debug("Adding spectrum stream for %s", detector.name)
main_data = self.main_app.main_data
stctrl = self._tab.streambar_controller
spg = stctrl._getAffectingSpectrograph(detector)
axes = {
"wavelength": ("wavelength", spg),
"grating": ("grating", spg),
"slit-in": ("slit-in", spg),
}
# Also add light filter for the spectrum stream if it affects the detector
for fw in (main_data.cl_filter, main_data.light_filter):
if fw is None:
continue
if detector.name in fw.affects.value:
axes["filter"] = ("band", fw)
break
axes = stctrl._filter_axes(axes)
if model.hasVA(main_data.ebeam, "blanker"):
blanker = main_data.ebeam.blanker
else:
logging.warning(
"E-beam doesn't support blanker, but trying to use a BlankerSpectrum stream"
)
blanker = None
spec_stream = BlSpectrumSettingsStream(
name,
detector,
detector.data,
main_data.ebeam,
sstage=main_data.scan_stage,
opm=main_data.opm,
axis_map=axes,
detvas=get_local_vas(detector, main_data.hw_settings_config),
blanker=blanker)
stctrl._set_default_spectrum_axes(spec_stream)
# Create the equivalent MDStream
sem_stream = self._tab.tab_data_model.semStream
sem_spec_stream = BlSEMSpectrumMDStream("SEM " + name,
[sem_stream, spec_stream])
ret = stctrl._addRepStream(spec_stream, sem_spec_stream)
# Force the ROI to full FoV, as for the alignment with the SEM image, we need to have always a full image
spec_stream.roi.value = (0, 0, 1, 1)
return ret
def __init__(self, stream):
super(SinglePointSpectrumProjection, self).__init__(stream)
self.stream.selected_pixel.subscribe(self._on_selected_pixel)
self.stream.selectionWidth.subscribe(self._on_selected_width)
if model.hasVA(self.stream, "selected_time"):
self.stream.selected_time.subscribe(self._on_selected_time)
self.stream.calibrated.subscribe(self._on_new_spec_data, init=True)
def start(self):
"""
Called when the menu entry is selected
"""
main_data = self.main_app.main_data
# Stop the streams
tab_data = main_data.tab.value.tab_data_model
for s in tab_data.streams.value:
s.should_update.value = False
self.filename.value = self._get_new_filename()
self._update_exp_dur()
# Special CCD settings to get values as photon counting
if model.hasVA(self.ccd, "countConvert"):
self.ccd.countConvert.value = 2 # photons
dlg = AcquisitionDialog(self, "Super-resolution acquisition",
"Acquires a series of shortly exposed images, "
"and store them in sequence.\n"
"Note, the advanced settings are only applied "
"after restarting the stream.")
dlg.addStream(self._stream)
dlg.addSettings(self, self.vaconf)
dlg.addButton("Close")
dlg.addButton("Acquire", self.acquire, face_colour='blue')
dlg.Maximize()
ans = dlg.ShowModal()
# Make sure the stream is not playing anymore and CCD is back to normal
self._stream.should_update.value = False
if model.hasVA(self.ccd, "countConvert"):
try:
self.ccd.countConvert.value = 0 # normal
except Exception:
logging.exception("Failed to set back count convert mode")
if ans == 0:
logging.info("Acquisition cancelled")
elif ans == 1:
logging.info("Acquisition completed")
else:
logging.warning("Got unknown return code %s", ans)
dlg.Destroy()
def test_temp(self):
if not model.hasVA(self.camera, "targetTemperature"):
self.skipTest("Camera doesn't support setting temperature")
ttemp = self.camera.targetTemperature.value
self.assertTrue(-300 < ttemp and ttemp < 100)
self.camera.targetTemperature.value = self.camera.targetTemperature.range[0]
self.assertEqual(self.camera.targetTemperature.value, self.camera.targetTemperature.range[0])
def __init__(self, name, role, dependencies, **kwargs):
"""
dependencies (dict str -> Emitter): arbitrary role -> emitter to be used as
part of this emitter. All its provided emissions will be provided.
"""
# TODO: allow to only use a subset of the emissions from each child
if not dependencies:
raise ValueError("MultiplexLight needs dependencies")
model.Emitter.__init__(self,
name,
role,
dependencies=dependencies,
**kwargs)
self._shape = ()
self._child_idx = {
} # Emitter -> index (shift) in the emissions/spectra
spectra = []
for n, child in dependencies.items():
if not (model.hasVA(child, "power") and model.hasVA(
child, "emissions") and model.hasVA(child, "spectra")):
raise ValueError("Child %s is not a light emitter" % (n, ))
self._child_idx[child] = len(spectra)
spectra.extend(child.spectra.value)
# TODO: update emissions whenever the child emissions change
# Child with the maximum power range
max_power = max(c.power.range[1] for c in self.dependencies.value)
self.power = model.FloatContinuous(0, (0., max_power), unit="W")
self.power.subscribe(self._updatePower)
# info on which source is which wavelength
self.spectra = model.ListVA(spectra, unit="m", readonly=True)
# It needs .spectra and .power
pwr, em = self._readPwrEmissions()
self.power._value = pwr
# ratio of power per source
# if some source don't support max power, clamped before 1
self.emissions = model.ListVA(em, unit="", setter=self._setEmissions)
def AutoFocusSpectrometer(spectrograph, focuser, detectors, selector=None):
"""
Run autofocus for a spectrograph. It will actually run autofocus on each
gratings, and for each detectors. The input slit should already be in a
good position (typically, almost closed), and a light source should be
active.
Note: it's currently tailored to the Andor Shamrock SR-193i. It's recommended
to put the detector on the "direct" output as first detector.
spectrograph (Actuator): should have grating and wavelength.
focuser (Actuator): should have a z axis
detectors (Detector or list of Detectors): all the detectors available on
the spectrometer. The first detector will be used to autofocus all the
gratings, and each other detector will be focused with the original
grating.
selector (Actuator or None): must have a rx axis with each position corresponding
to one of the detectors. If there is only one detector, selector can be None.
return (ProgressiveFuture -> dict((grating, detector)->focus position)): a progressive future
which will eventually return a map of grating/detector -> focus position.
"""
if not isinstance(detectors, collections.Iterable):
detectors = [detectors]
if not detectors:
raise ValueError("At least one detector must be provided")
if len(detectors) > 1 and selector is None:
raise ValueError("No selector provided, but multiple detectors")
# Create ProgressiveFuture and update its state to RUNNING
est_start = time.time() + 0.1
detector = detectors[0]
if model.hasVA(detector, "exposureTime"):
et = detector.exposureTime.value
else:
# Completely random... but we are in a case where probably that's the last
# thing the caller will care about.
et = 1
# 1 time / grating + 1 time / extra detector
cnts = len(spectrograph.axes["grating"].choices) + (len(detectors) - 1)
f = model.ProgressiveFuture(start=est_start,
end=est_start +
cnts * estimateAutoFocusTime(et))
f.task_canceller = _CancelAutoFocusSpectrometer
# Extra info for the canceller
f._autofocus_state = RUNNING
f._autofocus_lock = threading.Lock()
f._subfuture = InstantaneousFuture()
# Run in separate thread
autofocus_thread = threading.Thread(target=executeTask,
name="Spectrometer Autofocus",
args=(f, _DoAutoFocusSpectrometer, f,
spectrograph, focuser, detectors,
selector))
autofocus_thread.start()
return f
def __init__(self, stream):
super(LineSpectrumProjection, self).__init__(stream)
if model.hasVA(self.stream, "selected_time"):
self.stream.selected_time.subscribe(self._on_selected_time)
self.stream.selectionWidth.subscribe(self._on_selected_width)
self.stream.selected_line.subscribe(self._on_selected_line)
self.stream.calibrated.subscribe(self._on_new_data)
self._shouldUpdateImage()
def test_temp(self):
if not model.hasVA(self.camera, "targetTemperature"):
self.skipTest("Camera doesn't support setting temperature")
ttemp = self.camera.targetTemperature.value
self.assertTrue(-300 < ttemp < 100)
self.camera.targetTemperature.value = self.camera.targetTemperature.range[
0]
self.assertEqual(self.camera.targetTemperature.value,
self.camera.targetTemperature.range[0])
def test_basic(self):
"""
check the synchronization of the SEM with the CCD:
The SEM scans a region and for each point, the CCD acquires one image.
"""
start = time.time()
exp = 50e-3 # s
# in practice, it takes up to 500ms to take an image of 50 ms exposure
numbert = numpy.prod(self.sem_size)
# use large binning, to reduce the resolution
if model.hasVA(self.ccd, "binning") and not self.ccd.binning.readonly:
self.ccd.binning.value = self.ccd.binning.clip((4, 4))
self.ccd_size = self.ccd.resolution.value
self.ccd.exposureTime.value = exp
# magical formula to get a long enough dwell time.
# works with PVCam and Andorcam, but is probably different with other drivers :-(
readout = numpy.prod(self.ccd_size) / self.ccd.readoutRate.value
# it seems with the iVac, 20ms is enough to account for the overhead and extra image acquisition
self.scanner.dwellTime.value = (exp + readout) * 1.1 + 0.2
self.scanner.resolution.value = self.sem_size
# pixel write/read setup is pretty expensive ~10ms
expected_duration = numbert * (self.scanner.dwellTime.value + 0.01)
self.sem_left = 1 # unsubscribe just after one
self.ccd_left = numbert # unsubscribe after receiving
try:
self.ccd.data.synchronizedOn(self.scanner.newPosition)
except IOError:
self.skipTest("Camera doesn't support synchronisation")
self.ccd.data.subscribe(self.receive_ccd_image)
self.sed.data.subscribe(self.receive_sem_data)
for i in range(10):
# * 3 because it can be quite long to setup each pixel.
time.sleep(expected_duration * 2 / 10)
if self.sem_left == 0:
break # just to make it quicker if it's quicker
self.ccd.data.unsubscribe(self.receive_ccd_image)
self.sed.data.unsubscribe(self.receive_sem_data)
self.ccd.data.synchronizedOn(None)
logging.info("Took %g s", self.end_time - start)
time.sleep(exp + readout)
self.assertEqual(self.sem_left, 0)
self.assertEqual(self.ccd_left, 0)
# check we can still get data normally
d = self.ccd.data.get()
time.sleep(0.1)
def estimateAcquisitionTime(detector, scanner=None):
"""
Estimate how long one acquisition will take
detector (model.DigitalCamera or model.Detector): Detector on which to
improve the focus quality
scanner (None or model.Emitter): In case of a SED this is the scanner used
return (0<float): time in s
"""
# Check if there is a scanner (focusing = SEM)
if model.hasVA(scanner, "dwellTime"):
et = scanner.dwellTime.value * numpy.prod(scanner.resolution.value)
elif model.hasVA(detector, "exposureTime"):
et = detector.exposureTime.value
# TODO: also add readoutRate * resolution if present
else:
# Completely random... but we are in a case where probably that's the last
# thing the caller will care about.
et = 1
return et
def observeQWP(self, qwp, comp_affected):
if model.hasVA(qwp, "position"):
def updatePosition(pos, comp_affected=comp_affected):
md = {model.MD_POL_POS_QWP: pos["rz"]}
comp_affected.updateMetadata(md)
qwp.position.subscribe(updatePosition, init=True)
self._onTerminate.append((qwp.position.unsubscribe, (updatePosition,)))
return True
def observePolAnalyzer(self, analyzer, comp_affected):
if model.hasVA(analyzer, "position"):
def updatePosition(pos, comp_affected=comp_affected):
md = {model.MD_POL_MODE: pos["pol"]}
comp_affected.updateMetadata(md)
analyzer.position.subscribe(updatePosition, init=True)
self._onTerminate.append((analyzer.position.unsubscribe, (updatePosition,)))
return True
def observeLinPol(self, linpol, comp_affected):
if model.hasVA(linpol, "position"):
def updatePosition(pos, comp_affected=comp_affected):
md = {model.MD_POL_POS_LINPOL: pos["rz"]}
comp_affected.updateMetadata(md)
linpol.position.subscribe(updatePosition, init=True)
self._onTerminate.append((linpol.position.unsubscribe, (updatePosition,)))
return True
def test_basic(self):
"""
check the synchronization of the SEM with the CCD:
The SEM scans a region and for each point, the CCD acquires one image.
"""
start = time.time()
exp = 50e-3 # s
# in practice, it takes up to 500ms to take an image of 50 ms exposure
numbert = numpy.prod(self.sem_size)
# use large binning, to reduce the resolution
if model.hasVA(self.ccd, "binning") and not self.ccd.binning.readonly:
self.ccd.binning.value = self.ccd.binning.clip((4, 4))
self.ccd_size = self.ccd.resolution.value
self.ccd.exposureTime.value = exp
# magical formula to get a long enough dwell time.
# works with PVCam and Andorcam, but is probably different with other drivers :-(
readout = numpy.prod(self.ccd_size) / self.ccd.readoutRate.value
# it seems with the iVac, 20ms is enough to account for the overhead and extra image acquisition
self.scanner.dwellTime.value = (exp + readout) * 1.1 + 0.2
self.scanner.resolution.value = self.sem_size
# pixel write/read setup is pretty expensive ~10ms
expected_duration = numbert * (self.scanner.dwellTime.value + 0.01)
self.sem_left = 1 # unsubscribe just after one
self.ccd_left = numbert # unsubscribe after receiving
try:
self.ccd.data.synchronizedOn(self.scanner.newPosition)
except IOError:
self.skipTest("Camera doesn't support synchronisation")
self.ccd.data.subscribe(self.receive_ccd_image)
self.sed.data.subscribe(self.receive_sem_data)
for i in range(10):
# * 3 because it can be quite long to setup each pixel.
time.sleep(expected_duration * 2 / 10)
if self.sem_left == 0:
break # just to make it quicker if it's quicker
self.ccd.data.unsubscribe(self.receive_ccd_image)
self.sed.data.unsubscribe(self.receive_sem_data)
self.ccd.data.synchronizedOn(None)
logging.info("Took %g s", self.end_time - start)
time.sleep(exp + readout)
self.assertEqual(self.sem_left, 0)
self.assertEqual(self.ccd_left, 0)
# check we can still get data normally
d = self.ccd.data.get()
time.sleep(0.1)
def test_fan(self):
if not model.hasVA(self.camera, "fanSpeed"):
self.skipTest("Camera doesn't support setting fan speed")
orig_fs = self.camera.fanSpeed.value
self.camera.fanSpeed.value = self.camera.fanSpeed.range[0]
self.assertEqual(self.camera.fanSpeed.value, self.camera.fanSpeed.range[0])
self.camera.fanSpeed.value = self.camera.fanSpeed.range[1]
self.assertEqual(self.camera.fanSpeed.value, self.camera.fanSpeed.range[1])
self.camera.fanSpeed.value = orig_fs
self.assertEqual(self.camera.fanSpeed.value, orig_fs)
def __init__(self, name, role, children, **kwargs):
"""
children (dict str->Component): the two components to wrap together.
The key must be "light" for the emitter component, and "clock" for the clock generator.
"""
# This will create the .powerSupply VA
model.Emitter.__init__(self, name, role, children=children, **kwargs)
self._shape = ()
# Determine child objects. Light
try:
self._light = children["light"]
except KeyError:
raise ValueError("No 'light' child provided")
if not isinstance(self._light, model.ComponentBase):
raise ValueError("Child %s is not an emitter." % (self._light.name,))
if not model.hasVA(self._light, 'power'):
raise ValueError("Child %s has no power VA." % (self._light.name,))
if not model.hasVA(self._light, 'emissions'):
raise ValueError("Child %s has no emissions VA." % (self._light.name,))
# Clock generator
try:
self._clock = children["clock"]
except KeyError:
raise ValueError("No 'clock generator' child provided")
if not isinstance(self._clock, model.ComponentBase):
raise ValueError("Child %s is not a Component." % (self._clock.name,))
if not model.hasVA(self._clock, "period"):
raise ValueError("Child %s has no period VA." % (self._clock.name,))
# Only one VA from the clock
self.period = self._clock.period
# All the other VAs are straight from the light
self.emissions = self._light.emissions
self.spectra = self._light.spectra
self.power = self._light.power
# Turn off/on the power of the clock based on the light power
self.emissions.subscribe(self._onEmissions)
self.power.subscribe(self._onPower)
def __init__(self, name, role, children, **kwargs):
"""
children (dict str -> Emitter): arbitrary role -> emitter to be used as
part of this emitter. All its provided emissions will be provided.
"""
# TODO: allow to only use a subset of the emissions from each child
if not children:
raise ValueError("MultiplexLight needs children")
model.Emitter.__init__(self, name, role, children=children, **kwargs)
self._shape = ()
self._child_idx = {} # Emitter -> index (shift) in the emissions/spectra
spectra = []
for n, child in children.items():
if not (model.hasVA(child, "power") and
model.hasVA(child, "emissions") and
model.hasVA(child, "spectra")
):
raise ValueError("Child %s is not a light emitter" % (n,))
self._child_idx[child] = len(spectra)
spectra.extend(child.spectra.value)
# TODO: update emissions whenever the child emissions change
# Child with the maximum power range
max_power = max(c.power.range[1] for c in self.children.value)
self.power = model.FloatContinuous(0, (0., max_power), unit="W")
self.power.subscribe(self._updatePower)
# info on which source is which wavelength
self.spectra = model.ListVA(spectra, unit="m", readonly=True)
# It needs .spectra and .power
pwr, em = self._readPwrEmissions()
self.power._value = pwr
# ratio of power per source
# if some source don't support max power, clamped before 1
self.emissions = model.ListVA(em, unit="", setter=self._setEmissions)
def _estimate_step_duration(self):
"""
return (float > 0): estimated time (in s) that it takes to move the focus
by one step.
"""
speed = None
if model.hasVA(self.focus, "speed"):
speed = self.focus.speed.value.get('z', None)
if speed is None:
speed = 10e-6 # m/s, pessimistic
return driver.estimateMoveDuration(abs(self.zstep.value), speed, 0.01)
def AutoFocusSpectrometer(spectrograph, focuser, detectors, selector=None):
"""
Run autofocus for a spectrograph. It will actually run autofocus on each
gratings, and for each detectors. The input slit should already be in a
good position (typically, almost closed), and a light source should be
active.
Note: it's currently tailored to the Andor Shamrock SR-193i. It's recommended
to put the detector on the "direct" output as first detector.
spectrograph (Actuator): should have grating and wavelength.
focuser (Actuator): should have a z axis
detectors (Detector or list of Detectors): all the detectors available on
the spectrometer. The first detector will be used to autofocus all the
gratings, and each other detector will be focused with the original
grating.
selector (Actuator or None): must have a rx axis with each position corresponding
to one of the detectors. If there is only one detector, selector can be None.
return (ProgressiveFuture -> dict((grating, detector)->focus position)): a progressive future
which will eventually return a map of grating/detector -> focus position.
"""
if not isinstance(detectors, collections.Iterable):
detectors = [detectors]
if not detectors:
raise ValueError("At least one detector must be provided")
# Create ProgressiveFuture and update its state to RUNNING
est_start = time.time() + 0.1
detector = detectors[0]
if model.hasVA(detector, "exposureTime"):
et = detector.exposureTime.value
else:
# Completely random... but we are in a case where probably that's the last
# thing the caller will care about.
et = 1
# 1 time / grating + 1 time / extra detector
cnts = len(spectrograph.axes["grating"].choices) + (len(detectors) - 1)
f = model.ProgressiveFuture(start=est_start,
end=est_start + cnts * estimateAutoFocusTime(et))
f.task_canceller = _CancelAutoFocusSpectrometer
# Extra info for the canceller
f._autofocus_state = RUNNING
f._autofocus_lock = threading.Lock()
f._subfuture = InstantaneousFuture()
# Run in separate thread
autofocus_thread = threading.Thread(target=executeTask,
name="Spectrometer Autofocus",
args=(f, _DoAutoFocusSpectrometer, f,
spectrograph, focuser, detectors, selector))
autofocus_thread.start()
return f
def acquireTiledArea(stream, stage, area, live_stream=None):
"""
:param stream: (SEMStream) Stream used for the acquisition.
It must have the detector and emitter connected to the TFS XT client detector and scanner.
It should be in focus.
It must NOT have the following local VAs: horizontalFoV. resolution, scale
(because the VAs of the hardware will be changed directly, and so they shouldn’t be changed by the stream).
:param stage: (Actuator). It should have axes "x" and "y", which should already be referenced.
:param area: (float, float, float, float) minx, miny, maxx, maxy: coordinates of the overview region
:param live_stream: (StaticStream or None): StaticStream to be updated with
each tile acquired, to build up live the whole acquisition. NOT SUPPORTED YET.
: return: (ProgressiveFuture), acquisition future. It returns the complete DataArray.
"""
# Check the parameters
if len(area) != 4:
raise ValueError("area should be 4 float, but got %r" % (area, ))
for vaname in ("horizontalFoV", "resolution", "scale"):
if vaname in stream.emt_vas:
raise ValueError("Stream shouldn't have its own VA %s" %
(vaname, ))
if set(stage.axes) < {"x", "y"}:
raise ValueError("Stage needs axes x and y, but has %s" %
(stage.axes.keys(), ))
if model.hasVA(stage, "referenced"):
refd = stage.referenced.value
for a in ("x", "y"):
if a in refd:
if not refd[a]:
raise ValueError(
"Stage axis '%s' is not referenced. Reference it first"
% (a, ))
else:
logging.warning(
"Going to use the stage in absolute mode, but it doesn't report %s in .referenced VA",
a)
else:
logging.warning(
"Going to use the stage in absolute mode, but it doesn't have .referenced VA"
)
if live_stream:
raise NotImplementedError("live_stream not supported")
est_dur = estimateTiledAcquisitionTime(stream, stage, area)
f = model.ProgressiveFuture(start=time.time(), end=time.time() + est_dur)
_executor.submitf(f, _run_overview_acquisition, f, stream, stage, area,
live_stream)
return f
def test_fan(self):
if not model.hasVA(self.camera, "fanSpeed"):
self.skipTest("Camera doesn't support setting fan speed")
orig_fs = self.camera.fanSpeed.value
self.camera.fanSpeed.value = self.camera.fanSpeed.range[0]
self.assertEqual(self.camera.fanSpeed.value,
self.camera.fanSpeed.range[0])
self.camera.fanSpeed.value = self.camera.fanSpeed.range[1]
self.assertEqual(self.camera.fanSpeed.value,
self.camera.fanSpeed.range[1])
self.camera.fanSpeed.value = orig_fs
self.assertEqual(self.camera.fanSpeed.value, orig_fs)
def __init__(self, microscope, main_app):
super().__init__(microscope, main_app)
self.ebeam = main_app.main_data.ebeam
if not self.ebeam:
logging.info("No e-beam, plugin disabled ")
return
elif not model.hasVA(self.ebeam, "rotation"):
logging.info("e-beam has no rotation, plugin disabled ")
return
self.ebeam.rotation.subscribe(self._on_rotation, init=True)
def _estimateStreamPixels(self, s):
"""
return (int): the number of pixels the stream will generate during an
acquisition
"""
px = 0
if isinstance(s, MultipleDetectorStream):
for st in s.streams:
# For the EMStream of a SPARC MDStream, it's just one pixel per
# repetition (excepted in case of fuzzing, but let's be optimistic)
if isinstance(st, (EMStream, CLStream)):
px += 1
else:
px += self._estimateStreamPixels(st)
if hasattr(s, 'repetition'):
px *= s.repetition.value[0] * s.repetition.value[1]
return px
elif isinstance(s, (ARStream, SpectrumStream)):
# Temporarily reports 0 px, as we don't stitch these streams for now
return 0
if hasattr(s, 'emtResolution'):
px = numpy.prod(s.emtResolution.value)
elif hasattr(s, 'detResolution'):
px = numpy.prod(s.detResolution.value)
elif model.hasVA(s.detector, "resolution"):
px = numpy.prod(s.detector.resolution.value)
elif model.hasVA(s.emitter, "resolution"):
px = numpy.prod(s.emitter.resolution.value)
else:
# This shouldn't happen, but let's "optimistic" by assuming it'll
# only acquire one pixel.
logging.info("Resolution of stream %s cannot be determined.", s)
px = 1
return px
def _estimateStreamPixels(self, s):
"""
return (int): the number of pixels the stream will generate during an
acquisition
"""
px = 0
if isinstance(s, MultipleDetectorStream):
for st in s.streams:
# For the EMStream of a SPARC MDStream, it's just one pixel per
# repetition (excepted in case of fuzzing, but let's be optimistic)
if isinstance(st, (EMStream, CLStream)):
px += 1
else:
px += self._estimateStreamPixels(st)
if hasattr(s, 'repetition'):
px *= s.repetition.value[0] * s.repetition.value[1]
return px
elif isinstance(s, (ARStream, SpectrumStream)):
# Temporarily reports 0 px, as we don't stitch these streams for now
return 0
if hasattr(s, 'emtResolution'):
px = numpy.prod(s.emtResolution.value)
elif hasattr(s, 'detResolution'):
px = numpy.prod(s.detResolution.value)
elif model.hasVA(s.detector, "resolution"):
px = numpy.prod(s.detector.resolution.value)
elif model.hasVA(s.emitter, "resolution"):
px = numpy.prod(s.emitter.resolution.value)
else:
# This shouldn't happen, but let's "optimistic" by assuming it'll
# only acquire one pixel.
logging.info("Resolution of stream %s cannot be determined.", s)
px = 1
return px
def __init__(self, name, role, dependencies, **kwargs):
"""
dependencies (dict str -> Emitter): arbitrary role -> emitter to be used as
part of this emitter. All its provided emissions will be provided.
"""
# TODO: allow to only use a subset of the emissions from each child
if not dependencies:
raise ValueError("MultiplexLight needs dependencies")
model.Emitter.__init__(self, name, role, dependencies=dependencies, **kwargs)
self._shape = ()
self._child_idx = {} # Emitter -> index (shift) in the power/spectra
spectra = []
min_power = []
max_power = []
for n, child in dependencies.items():
if not (model.hasVA(child, "power") and
model.hasVA(child, "spectra")
):
raise ValueError("Child %s is not a light emitter" % (n,))
self._child_idx[child] = len(spectra)
spectra.extend(child.spectra.value)
min_power.extend(child.power.range[0])
max_power.extend(child.power.range[1])
# Subscribe to each child power to update self.power
child.power.subscribe(self._updateMultiplexPower)
# Child with the maximum power range
self.power = model.ListContinuous(value=[0] * len(spectra),
range=(tuple(min_power), tuple(max_power)),
unit="W", cls=(int, long, float))
self.power.subscribe(self._setChildPower)
self._updateMultiplexPower(None)
# info on which source is which wavelength
self.spectra = model.ListVA(spectra, unit="m", readonly=True)
def _updateReferenced(self):
"""
update the referenced VA
"""
ref = {} # str (axes name) -> boolean (is referenced)
# consider an axis referenced iff it's referenced in every referenceable children
for c in self.children.value:
if not model.hasVA(c, "referenced"):
continue
cref = c.referenced.value
for a in (set(self.axes.keys()) & set(cref.keys())):
ref[a] = ref.get(a, True) and cref[a]
self.referenced._set_value(ref, force_write=True)
def _updateReferenced(self):
"""
update the referenced VA
"""
ref = {} # str (axes name) -> boolean (is referenced)
# consider an axis referenced iff it's referenced in every referenceable children
for c in self.children.value:
if not model.hasVA(c, "referenced"):
continue
cref = c.referenced.value
for a in (set(self.axes.keys()) & set(cref.keys())):
ref[a] = ref.get(a, True) and cref[a]
self.referenced._set_value(ref, force_write=True)
def __init__(self, tab_panel, tab_data, highlight_change=False):
super(SecomSettingsController, self).__init__(tab_data)
main_data = tab_data.main
self._sem_panel = SemSettingsController(
tab_panel.fp_settings_secom_sem, "No SEM found", highlight_change,
tab_data)
self._optical_panel = OpticalSettingsController(
tab_panel.fp_settings_secom_optical, "No optical microscope found",
highlight_change, tab_data)
# Add the components based on what is available
# TODO: move it to a separate thread to save time at init?
if main_data.ccd:
# Hide exposureTime as it's in local settings of the stream
self.add_hw_component(main_data.ccd,
self._optical_panel,
hidden={"exposureTime"})
if hasattr(tab_data, "confocal_set_stream"):
conf_set_e = StreamController(tab_panel.pnl_opt_streams,
tab_data.confocal_set_stream,
tab_data)
conf_set_e.stream_panel.flatten() # removes the expander header
# StreamController looks pretty much the same as SettingController
self.setting_controllers.append(conf_set_e)
else:
tab_panel.pnl_opt_streams.Hide() # Not needed
# For now, we assume that the pinhole (axis) is global: valid for all
# the confocal streams and FLIM stream. That's partly because most likely
# the user wouldn't want to have separate values... and also because
# anyway we don't currently support local stream axes.
if main_data.pinhole:
conf = get_hw_config(main_data.pinhole, self._hw_settings_config)
for a in ("d", ):
if a not in main_data.pinhole.axes:
continue
self._optical_panel.add_axis(a, main_data.pinhole, conf.get(a))
if main_data.ebeam:
self.add_hw_component(main_data.ebeam, self._sem_panel)
# If can do AutoContrast, display the button
# TODO: check if detector has a .applyAutoContrast() method, instead
# of detecting indirectly via the presence of .bpp.
det = main_data.sed or main_data.bsd
if det and model.hasVA(det, "bpp"):
self._sem_panel.add_bc_control(det)
def _computeSpec(self):
"""
Compute the spectrum from the stream with the current parameters.
Returns: a 1-D DataArray or None if the spectrum could not be computed
"""
data = self.stream.calibrated.value
if (self.stream.selected_pixel.value == (None, None) or
data is None or data.shape[0] == 1):
return None
x, y = self.stream.selected_pixel.value
if model.hasVA(self.stream, "selected_time"):
t = self.stream._tl_px_values.index(self.stream.selected_time.value)
else:
t = 0
spec2d = self.stream.calibrated.value[:, t, 0, :, :] # same data but remove useless dims
md = dict(data.metadata)
md[model.MD_DIMS] = "C"
# We treat width as the diameter of the circle which contains the center
# of the pixels to be taken into account
width = self.stream.selectionWidth.value
if width == 1: # short-cut for simple case
data = spec2d[:, y, x]
return model.DataArray(data, md)
# There are various ways to do it with numpy. As typically the spectrum
# dimension is big, and the number of pixels to sum is small, it seems
# the easiest way is to just do some kind of "clever" mean. Using a
# masked array would also work, but that'd imply having a huge mask.
radius = width / 2
n = 0
# TODO: use same cleverness as mean() for dtype?
datasum = numpy.zeros(spec2d.shape[0], dtype=numpy.float64)
# Scan the square around the point, and only pick the points in the circle
for px in range(max(0, int(x - radius)),
min(int(x + radius) + 1, spec2d.shape[-1])):
for py in range(max(0, int(y - radius)),
min(int(y + radius) + 1, spec2d.shape[-2])):
if math.hypot(x - px, y - py) <= radius:
n += 1
datasum += spec2d[:, py, px]
mean = datasum / n
return model.DataArray(mean.astype(spec2d.dtype), md)
def _set_blanker(escan, active):
"""
Set the blanker to the given state iif the blanker doesn't support "automatic"
mode (ie, None).
escan (ebeam scanner)
active (bool): True = blanking = no ebeam
"""
try:
if (model.hasVA(escan, "blanker")
and not None in escan.blanker.choices):
# Note: we assume that this is blocking, until the e-beam is
# ready to acquire an image.
escan.blanker.value = active
except Exception:
logging.exception("Failed to set the blanker to %s", active)
def _set_fan(self, enable):
"""
Turn on/off the fan of the CCD
enable (boolean): True to turn on/restore the fan, and False to turn if off
"""
if model.hasVA(self._main_data_model.ccd, "fanSpeed"):
return
fs = self._main_data_model.ccd.fanSpeed
if enable:
if self._orig_fan_speed is not None:
fs.value = max(fs.value, self._orig_fan_speed)
else:
self._orig_fan_speed = fs.value
fs.value = 0
def __init__(self, tab_panel, tab_data, highlight_change=False):
super(SecomSettingsController, self).__init__(tab_data)
main_data = tab_data.main
self._sem_panel = SemSettingsController(tab_panel.fp_settings_secom_sem,
"No SEM found",
highlight_change,
tab_data)
self._optical_panel = OpticalSettingsController(tab_panel.fp_settings_secom_optical,
"No optical microscope found",
highlight_change,
tab_data)
# Add the components based on what is available
# TODO: move it to a separate thread to save time at init?
if main_data.ccd:
# Hide exposureTime as it's in local settings of the stream
self.add_hw_component(main_data.ccd, self._optical_panel, hidden={"exposureTime"})
if hasattr(tab_data, "confocal_set_stream"):
conf_set_e = StreamController(tab_panel.pnl_opt_streams, tab_data.confocal_set_stream, tab_data)
conf_set_e.stream_panel.flatten() # removes the expander header
# StreamController looks pretty much the same as SettingController
self.setting_controllers.append(conf_set_e)
else:
tab_panel.pnl_opt_streams.Hide() # Not needed
# For now, we assume that the pinhole (axis) is global: valid for all
# the confocal streams and FLIM stream. That's partly because most likely
# the user wouldn't want to have separate values... and also because
# anyway we don't currently support local stream axes.
if main_data.pinhole:
conf = get_hw_config(main_data.pinhole, self._hw_settings_config)
for a in ("d",):
if a not in main_data.pinhole.axes:
continue
self._optical_panel.add_axis(a, main_data.pinhole, conf.get(a))
if main_data.ebeam:
self.add_hw_component(main_data.ebeam, self._sem_panel)
# If can do AutoContrast, display the button
# TODO: check if detector has a .applyAutoContrast() method, instead
# of detecting indirectly via the presence of .bpp.
det = main_data.sed or main_data.bsd
if det and model.hasVA(det, "bpp"):
self._sem_panel.add_bc_control(det)
def _find_spectrometer(self, detector):
"""
Find a spectrometer which wraps the given detector
return (Detector): the spectrometer
raise LookupError: if nothing found.
"""
for spec in self.main_app.main_data.spectrometers:
# Check by name as the components are actually Pyro proxies, which
# might not be equal even if they point to the same component.
if (model.hasVA(spec, "dependencies")
and detector.name in (d.name
for d in spec.dependencies.value)):
return spec
raise LookupError("No spectrometer corresponding to %s found" %
(detector.name, ))
def test_software_trigger(self):
"""
Check that the synchronisation with softwareTrigger works.
Make it typical, by waiting for the data received, and then notifying
the software trigger again after a little while.
"""
if not hasattr(self.ccd, "softwareTrigger"):
self.skipTest("Camera doesn't support software trigger")
self.ccd.exposureTime.value = self.ccd.exposureTime.clip(50e-3) # s
exp = self.ccd.exposureTime.value
if model.hasVA(self.ccd, "binning") and not self.ccd.binning.readonly:
self.ccd.binning.value = self.ccd.binning.clip((1, 1))
self.ccd_size = self.ccd.resolution.value
readout = numpy.prod(self.ccd_size) / self.ccd.readoutRate.value
duration = exp + readout # approximate time for one frame
numbert = 10
self.ccd_left = numbert # unsubscribe after receiving
try:
self.ccd.data.synchronizedOn(self.ccd.softwareTrigger)
except IOError:
self.skipTest("Camera doesn't support synchronisation")
self.ccd.data.subscribe(self.receive_ccd_image)
# Wait for the image
for i in range(numbert):
self.got_image.clear()
self.ccd.softwareTrigger.notify()
# wait for the image to be received
gi = self.got_image.wait(duration + 10)
self.assertTrue(gi,
"image not received after %g s" % (duration + 10))
time.sleep(i * 0.1) # wait a bit to simulate some processing
self.assertEqual(self.ccd_left, 0)
self.ccd.data.synchronizedOn(None)
# check we can still get data normally
d = self.ccd.data.get()
time.sleep(0.1)
def estimateAcquisitionTime(self, dt, shape):
# It's pretty easy to know how many times the leech will run, it's a lot
# harder to know how long it takes to acquire one probe current reading.
nacqs = 1 + math.ceil(dt * numpy.prod(shape) / self.period.value)
if model.hasVA(self._detector, "dwellTime"):
at = self._detector.dwellTime.value
else:
at = 0.1
if self._selector:
# The time it takes probably depends a lot on the hardware, and
# there is not much info (maybe the .speed could be used).
# For now, we just use the time the only hardware we support takes
at += 3.0 * 2 # doubled as it has go back and forth
at += 0.1 # for overhead
return nacqs * at
def acquire(self):
"""
Runs the find overlay procedure
returns Future that will have as a result an empty DataArray with
the correction metadata
"""
# Just calls the FindOverlay function and return its future
ovrl_future = align.FindOverlay(self.repetition.value,
self.dwellTime.value,
OVRL_MAX_DIFF,
self._emitter,
self._ccd,
self._detector,
skew=True,
bgsub=model.hasVA(self._emitter, "blanker"))
ovrl_future.result = self._result_wrapper(ovrl_future.result)
return ovrl_future
def test_software_trigger(self):
"""
Check that the synchronisation with softwareTrigger works.
Make it typical, by waiting for the data received, and then notifying
the software trigger again after a little while.
"""
if not hasattr(self.ccd, "softwareTrigger"):
self.skipTest("Camera doesn't support software trigger")
self.ccd.exposureTime.value = self.ccd.exposureTime.clip(50e-3) # s
exp = self.ccd.exposureTime.value
if model.hasVA(self.ccd, "binning") and not self.ccd.binning.readonly:
self.ccd.binning.value = self.ccd.binning.clip((1, 1))
self.ccd_size = self.ccd.resolution.value
readout = numpy.prod(self.ccd_size) / self.ccd.readoutRate.value
duration = exp + readout # approximate time for one frame
numbert = 10
self.ccd_left = numbert # unsubscribe after receiving
try:
self.ccd.data.synchronizedOn(self.ccd.softwareTrigger)
except IOError:
self.skipTest("Camera doesn't support synchronisation")
self.ccd.data.subscribe(self.receive_ccd_image)
# Wait for the image
for i in range(numbert):
self.got_image.clear()
self.ccd.softwareTrigger.notify()
# wait for the image to be received
gi = self.got_image.wait(duration + 10)
self.assertTrue(gi, "image not received after %g s" % (duration + 10))
time.sleep(i * 0.1) # wait a bit to simulate some processing
self.assertEqual(self.ccd_left, 0)
self.ccd.data.synchronizedOn(None)
# check we can still get data normally
d = self.ccd.data.get()
time.sleep(0.1)
def __init__(self, name, role, children, **kwargs):
'''
children (dict string->model.HwComponent): the children
There must be exactly two children "external" and "internal".
Raise:
ValueError: if the children are not compatible
'''
# we will fill the set of children with Components later in ._children
model.Emitter.__init__(self, name, role, **kwargs)
# Check the children
extnl = children["external"]
if not isinstance(extnl, ComponentBase):
raise ValueError("Child external is not a component.")
if not model.hasVA(extnl, "pixelSize"):
raise ValueError("Child external is not a Emitter component.")
self._external = extnl
self.children.value.add(extnl)
intnl = children["internal"]
if not isinstance(intnl, ComponentBase):
raise ValueError("Child internal is not a component.")
if not model.hasVA(intnl, "pixelSize"):
raise ValueError("Child internal is not a Emitter component.")
self._internal = intnl
self.children.value.add(intnl)
# Copy VAs directly related to scanning from external
self._shape = self._external.shape
for vaname in ("pixelSize", "translation", "resolution", "scale",
"rotation", "dwellTime"):
if model.hasVA(self._external, vaname):
va = getattr(self._external, vaname)
setattr(self, vaname, va)
# Copy VAs for controlling the ebeam from internal
# horizontalFoV or magnification need a bit more cleverness
if model.hasVA(self._internal, "horizontalFoV"):
self.horizontalFoV = self._internal.horizontalFoV
# Create read-only magnification VA
mag = self._external.HFWNoMag / self.horizontalFoV.value
self.magnification = model.VigilantAttribute(mag, unit="", readonly=True)
self.horizontalFoV.subscribe(self._updateMagnification, init=True)
elif model.hasVA(self._external, "magnification"):
self.magnification = self._external.magnification
# TODO: just pick every VAs which are not yet on self?
for vaname in ("accelVoltage", "power", "probeCurrent"):
if model.hasVA(self._internal, vaname):
va = getattr(self._internal, vaname)
setattr(self, vaname, va)
def _doReference(self, axes):
fs = []
for c in self.children.value:
# only do the referencing for the stages that support it
if not model.hasVA(c, "referenced"):
continue
ax = axes & set(c.referenced.value.keys())
fs.append(c.reference(ax))
# wait for all referencing to be over
for f in fs:
f.result()
# Re-synchronize the 2 stages by moving the slave where the master is
mpos = self._master.position.value
f = self._stage_conv.moveAbs({"x": mpos["x"], "y": mpos["y"]})
f.result()
self._updatePosition()
def _doReference(self, axes):
fs = []
for c in self.children.value:
# only do the referencing for the stages that support it
if not model.hasVA(c, "referenced"):
continue
ax = axes & set(c.referenced.value.keys())
fs.append(c.reference(ax))
# wait for all referencing to be over
for f in fs:
f.result()
# Re-synchronize the 2 stages by moving the slave where the master is
mpos = self._master.position.value
f = self._stage_conv.moveAbs({"x": mpos["x"], "y": mpos["y"]})
f.result()
self._updatePosition()
def test_translation(self):
"""
test the translation VA (if available)
"""
if (not model.hasVA(self.camera, "translation")
or self.camera.translation.readonly):
self.skipTest("Camera doesn't support setting translation")
# Check the translation can be changed
self.camera.binning.value = (2, 2)
self.camera.resolution.value = (16, 16)
self.camera.translation.value = (
-10, 3) # values are small enough they should always be fine
im = self.camera.data.get()
self.assertEqual(self.camera.translation.value, (-10, 3))
# Check the translation automatically fits after putting a large ROI
self.camera.binning.value = (1, 1)
self.camera.resolution.value = self.camera.resolution.range[1]
self.assertEqual(self.camera.translation.value, (0, 0))
im = self.camera.data.get()
# Check the MD_POS metadata is correctly updated
orig_md = {
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m
model.MD_PIXEL_SIZE_COR: (0.5, 0.5), # the actual pxs is /2
model.MD_POS: (-1.1, 0.9),
}
self.camera.updateMetadata(orig_md)
im = self.camera.data.get()
self.assertEqual(im.metadata[model.MD_POS], orig_md[model.MD_POS])
self.camera.binning.value = (2, 2)
self.camera.updateMetadata({model.MD_PIXEL_SIZE: (2e-6, 2e-6)})
self.camera.resolution.value = (16, 16)
im = self.camera.data.get()
self.assertEqual(im.metadata[model.MD_POS], orig_md[model.MD_POS])
self.camera.translation.value = (-10, 3)
im = self.camera.data.get()
exp_pos = (-1.1 + (-10 * 2e-6 * 0.5), 0.9 - (3 * 2e-6 * 0.5)
) # phys Y goes opposite direction
self.assertEqual(im.metadata[model.MD_POS], exp_pos)
def test_translation(self):
"""
test the translation VA (if available)
"""
if (not model.hasVA(self.camera, "translation") or
self.camera.translation.readonly):
self.skipTest("Camera doesn't support setting translation")
# Check the translation can be changed
self.camera.binning.value = (2, 2)
self.camera.resolution.value = (16, 16)
self.camera.translation.value = (-10, 3) # values are small enough they should always be fine
im = self.camera.data.get()
self.assertEqual(self.camera.translation.value, (-10, 3))
# Check the translation automatically fits after putting a large ROI
self.camera.binning.value = (1, 1)
self.camera.resolution.value = self.camera.resolution.range[1]
self.assertEqual(self.camera.translation.value, (0, 0))
im = self.camera.data.get()
# Check the MD_POS metadata is correctly updated
orig_md = {model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m
model.MD_PIXEL_SIZE_COR: (0.5, 0.5), # the actual pxs is /2
model.MD_POS: (-1.1, 0.9),
}
self.camera.updateMetadata(orig_md)
im = self.camera.data.get()
self.assertEqual(im.metadata[model.MD_POS], orig_md[model.MD_POS])
self.camera.binning.value = (2, 2)
self.camera.updateMetadata({model.MD_PIXEL_SIZE: (2e-6, 2e-6)})
self.camera.resolution.value = (16, 16)
im = self.camera.data.get()
self.assertEqual(im.metadata[model.MD_POS], orig_md[model.MD_POS])
self.camera.translation.value = (-10, 3)
im = self.camera.data.get()
exp_pos = (-1.1 + (-10 * 2e-6 * 0.5), 0.9 - (3 * 2e-6 * 0.5)) # phys Y goes opposite direction
self.assertEqual(im.metadata[model.MD_POS], exp_pos)
def test_binning(self):
if not model.hasVA(self.camera,
"binning") or self.camera.binning.readonly:
self.skipTest("Camera doesn't support setting binning")
self.camera.binning.value = (1, 1)
if hasattr(self.camera.binning, "range"):
max_binning = self.camera.binning.range[1]
else: # if binning-VA is VAEnumerated
max_binning = max(self.camera.binning.choices)
new_binning = (2, 2)
if new_binning >= max_binning:
# if there is no binning 2, let's not try
self.skipTest("Camera doesn't support binning")
# binning should automatically resize the image
prev_size = self.camera.resolution.value
self.camera.binning.value = new_binning
self.assertNotEqual(self.camera.resolution.value, prev_size)
# ask for the whole image
self.size = (self.camera.shape[0] // 2, self.camera.shape[1] // 2)
self.camera.resolution.value = self.size
exposure = 0.1
self.camera.exposureTime.value = exposure
start = time.time()
im = self.camera.data.get()
duration = time.time() - start
self.assertEqual(
im.shape, self.size[::-1]
) # TODO a small size diff is fine if bigger than requested
self.assertGreaterEqual(
duration, exposure,
"Error execution took %f s, less than exposure time %f." %
(duration, exposure))
self.assertIn(model.MD_EXP_TIME, im.metadata)
self.assertEqual(im.metadata[model.MD_BINNING], new_binning)
def _projectTile(self, tile):
"""
Project the tile
tile (DataArray): Raw tile
return (DataArray): Projected tile
"""
dims = tile.metadata.get(model.MD_DIMS, "CTZYX"[-tile.ndim::])
ci = dims.find("C") # -1 if not found
# is RGB
if dims in ("CYX", "YXC") and tile.shape[ci] in (3, 4):
# Just pass the RGB data on
tile = img.ensureYXC(tile)
tile.flags.writeable = False
# merge and ensures all the needed metadata is there
tile.metadata = self.stream._find_metadata(tile.metadata)
tile.metadata[model.MD_DIMS] = "YXC" # RGB format
return tile
elif dims in ("ZYX",) and model.hasVA(self.stream, "zIndex"):
tile = img.getYXFromZYX(tile, self.stream.zIndex.value)
tile.metadata[model.MD_DIMS] = "ZYX"
else:
tile = img.ensure2DImage(tile)
return self._projectXY2RGB(tile, self.stream.tint.value)
def __init__(self, stream):
'''
stream (Stream): the Stream to project
'''
super(RGBSpatialProjection, self).__init__(stream)
# handle z stack
if model.hasVA(stream, "zIndex"):
self.zIndex = stream.zIndex
self.zIndex.subscribe(self._onZIndex)
if stream.raw and isinstance(stream.raw[0], model.DataArrayShadow):
# The raw tiles corresponding to the .image, updated whenever .image is updated
self._raw = (()) # 2D tuple of DataArrays
raw = stream.raw[0]
md = raw.metadata
# get the pixel size of the full image
ps = md[model.MD_PIXEL_SIZE]
max_mpp = ps[0] * (2 ** raw.maxzoom)
# sets the mpp as the X axis of the pixel size of the full image
mpp_rng = (ps[0], max_mpp)
self.mpp = model.FloatContinuous(max_mpp, mpp_rng, setter=self._set_mpp)
full_rect = img._getBoundingBox(raw)
l, t, r, b = full_rect
rect_range = ((l, b, l, b), (r, t, r, t))
self.rect = model.TupleContinuous(full_rect, rect_range)
self.mpp.subscribe(self._onMpp)
self.rect.subscribe(self._onRect)
# initialize the projected tiles cache
self._projectedTilesCache = {}
# initialize the raw tiles cache
self._rawTilesCache = {}
# When True, the projected tiles cache should be invalidated
self._projectedTilesInvalid = True
self._shouldUpdateImage()
def __init__(self, name, role, children, **kwargs):
'''
children (dict string->model.HwComponent): the children
There must be exactly two children "external" and "internal".
Raise:
ValueError: if the children are not compatible
'''
# we will fill the set of children with Components later in ._children
model.Emitter.__init__(self, name, role, **kwargs)
# Check the children
extnl = children["external"]
if not isinstance(extnl, ComponentBase):
raise ValueError("Child external is not a component.")
if not model.hasVA(extnl, "pixelSize"):
raise ValueError("Child external is not a Emitter component.")
self._external = extnl
self.children.value.add(extnl)
intnl = children["internal"]
if not isinstance(intnl, ComponentBase):
raise ValueError("Child internal is not a component.")
if not hasattr(intnl, "shape"):
# Note: the internal component doesn't need to provide pixelSize
raise ValueError("Child internal is not a Emitter component.")
self._internal = intnl
self.children.value.add(intnl)
# Copy VAs directly related to scanning from external
self._shape = self._external.shape
for vaname in ("pixelSize", "translation", "resolution", "scale",
"rotation", "dwellTime"):
if model.hasVA(self._external, vaname):
va = getattr(self._external, vaname)
setattr(self, vaname, va)
# Copy VAs for controlling the ebeam from internal
# horizontalFoV or magnification need a bit more cleverness
if model.hasVA(self._internal, "horizontalFoV"):
self.horizontalFoV = self._internal.horizontalFoV
# Create read-only magnification VA
# TODO: why not just using the magnification VA from the internal?
self.magnification = model.VigilantAttribute(1, unit="", readonly=True)
self.horizontalFoV.subscribe(self._updateMagnification, init=True)
elif model.hasVA(self._external, "magnification"):
self.magnification = self._external.magnification
# TODO: just pick every VAs which are not yet on self?
for vaname in ("accelVoltage", "probeCurrent", "depthOfField", "spotSize"):
if model.hasVA(self._internal, vaname):
va = getattr(self._internal, vaname)
setattr(self, vaname, va)
# VAs that could be both on internal or external. If on both, pick internal
# TODO: add a better way to select if both provide: either via arg, or
# select the one which provides a None (=auto)?
for vaname in ("power", "blanker", "external"):
if model.hasVA(self._internal, vaname):
va = getattr(self._internal, vaname)
setattr(self, vaname, va)
elif model.hasVA(self._external, vaname):
va = getattr(self._external, vaname)
setattr(self, vaname, va)
def _computeSpec(self):
"""
Compute the 1D spectrum from the stream.calibrated VA using the
selected_time, selected_line, and width.
return: spec1d, md
where spec1d is the line spectrum (1D array)
and md is the metadata structure
"""
if ((None, None) in self.stream.selected_line.value or
self.stream.calibrated.value.shape[0] == 1):
return None, None
if model.hasVA(self.stream, "selected_time"):
t = self.stream._tl_px_values.index(self.stream.selected_time.value)
else:
t = 0
spec2d = self.stream.calibrated.value[:, t, 0, :, :] # same data but remove useless dims
width = self.stream.selectionWidth.value
# Number of points to return: the length of the line
start, end = self.stream.selected_line.value
v = (end[0] - start[0], end[1] - start[1])
l = math.hypot(*v)
n = 1 + int(l)
if l < 1: # a line of just one pixel is considered not valid
return None, None
# FIXME: if the data has a width of 1 (ie, just a line), and the
# requested width is an even number, the output is empty (because all
# the interpolated points are outside of the data.
# Coordinates of each point: ndim of data (5-2), pos on line (Y), spectrum (X)
# The line is scanned from the end till the start so that the spectra
# closest to the origin of the line are at the bottom.
coord = numpy.empty((3, width, n, spec2d.shape[0]))
coord[0] = numpy.arange(spec2d.shape[0]) # spectra = all
coord_spc = coord.swapaxes(2, 3) # just a view to have (line) space as last dim
coord_spc[-1] = numpy.linspace(end[0], start[0], n) # X axis
coord_spc[-2] = numpy.linspace(end[1], start[1], n) # Y axis
# Spread over the width
# perpendicular unit vector
pv = (-v[1] / l, v[0] / l)
width_coord = numpy.empty((2, width))
spread = (width - 1) / 2
width_coord[-1] = numpy.linspace(pv[0] * -spread, pv[0] * spread, width) # X axis
width_coord[-2] = numpy.linspace(pv[1] * -spread, pv[1] * spread, width) # Y axis
coord_cw = coord[1:].swapaxes(0, 2).swapaxes(1, 3) # view with coordinates and width as last dims
coord_cw += width_coord
# Interpolate the values based on the data
if width == 1:
# simple version for the most usual case
spec1d = ndimage.map_coordinates(spec2d, coord[:, 0, :, :], order=1)
else:
# FIXME: the mean should be dependent on how many pixels inside the
# original data were pick on each line. Currently if some pixels fall
# out of the original data, the outside pixels count as 0.
# force the intermediate values to float, as mean() still needs to run
spec1d_w = ndimage.map_coordinates(spec2d, coord, output=numpy.float, order=1)
spec1d = spec1d_w.mean(axis=0).astype(spec2d.dtype)
assert spec1d.shape == (n, spec2d.shape[0])
# Use metadata to indicate spatial distance between pixel
pxs_data = self.stream.calibrated.value.metadata[MD_PIXEL_SIZE]
if pxs_data[0] is not None:
pxs = math.hypot(v[0] * pxs_data[0], v[1] * pxs_data[1]) / (n - 1)
else:
logging.warning("Pixel size should have two dimensions")
return None, None
raw_md = self.stream.calibrated.value.metadata
md = raw_md.copy()
md[model.MD_DIMS] = "XC" # RGB format
md[MD_PIXEL_SIZE] = (None, pxs) # for the spectrum, use get_spectrum_range()
return spec1d, md
def add_bc_control(self, detector):
""" Add Hw brightness/contrast control """
self.panel.add_divider()
# Create extra gird bag sizer
gb_sizer = wx.GridBagSizer()
gb_sizer.SetEmptyCellSize((0, 0))
# Create the widgets
btn_autoadjust = ImageTextToggleButton(self.panel, height=24, label="Auto adjust",
icon=img.getBitmap("icon/ico_contrast.png"))
btn_autoadjust.SetToolTip("Adjust detector brightness/contrast")
gb_sizer.Add(btn_autoadjust, (0, 0), (2, 1), border=10,
flag=wx.ALIGN_CENTRE_VERTICAL | wx.RIGHT)
sld_conf = {
"accuracy": 2,
"event": wx.EVT_SCROLL_CHANGED,
"control_type": odemis.gui.CONTROL_SLIDER,
"type": "slider",
}
num_rows = 0
if model.hasVA(detector, "brightness"):
brightness_entry = self.add_setting_entry("brightness", detector.brightness, detector,
sld_conf)
# TODO: 'Ugly' detaching somewhat nullifies the cleanliness created by using
# 'add_setting_entry'. 'add_setting_entry' Needs some more refactoring anyway.
self.panel.gb_sizer.Detach(brightness_entry.value_ctrl)
self.panel.gb_sizer.Detach(brightness_entry.lbl_ctrl)
gb_sizer.Add(brightness_entry.lbl_ctrl, (num_rows, 1))
gb_sizer.Add(brightness_entry.value_ctrl, (num_rows, 2), flag=wx.EXPAND)
num_rows += 1
if model.hasVA(detector, "contrast"):
contrast_entry = self.add_setting_entry("contrast", detector.contrast, detector,
sld_conf)
self.panel.gb_sizer.Detach(contrast_entry.value_ctrl)
self.panel.gb_sizer.Detach(contrast_entry.lbl_ctrl)
gb_sizer.Add(contrast_entry.lbl_ctrl, (num_rows, 1))
gb_sizer.Add(contrast_entry.value_ctrl, (num_rows, 2), flag=wx.EXPAND)
num_rows += 1
if num_rows:
gb_sizer.AddGrowableCol(2)
# Add the extra sizer to the main sizer
self.panel.gb_sizer.Add(gb_sizer, (self.panel.num_rows, 0), span=(1, 2),
border=5, flag=wx.ALL | wx.EXPAND)
self.panel.num_rows += 1
# Connect various events to the auto adjust button
def on_chamber_state(state, btn=btn_autoadjust):
wx.CallAfter(btn.Enable, state in (CHAMBER_UNKNOWN, CHAMBER_VACUUM))
# We keep a reference to keep the subscription active.
self._subscriptions.append(on_chamber_state)
self.tab_data.main.chamberState.subscribe(on_chamber_state, init=True)
def adjust_done(_):
""" Callback that enables and untoggles the 'auto adjust' contrast button """
btn_autoadjust.SetToggle(False)
btn_autoadjust.SetLabel("Auto adjust")
btn_autoadjust.Enable()
brightness_entry.value_ctrl.Enable()
contrast_entry.value_ctrl.Enable()
def auto_adjust(_):
""" Call the auto contrast method on the detector if it's not already running """
if not btn_autoadjust.up:
f = detector.applyAutoContrast()
btn_autoadjust.SetLabel("Adjusting...")
btn_autoadjust.Disable()
brightness_entry.value_ctrl.Disable()
contrast_entry.value_ctrl.Disable()
f.add_done_callback(adjust_done)
btn_autoadjust.Bind(wx.EVT_BUTTON, auto_adjust)