def test_continuous(self):
prop = model.FloatContinuous(2.0, [-1, 3.4])
self.assertEqual(prop.value, 2)
self.assertEqual(prop.range, (-1, 3.4))
self.called = 0
prop.subscribe(self.callback_test_notify)
# now count
prop.value = 3.0 # +1
self.assertEqual(prop.value, 3)
try:
prop.value = 4.0
self.fail("Assigning out of bound should not be allowed.")
except IndexError:
pass # as it should be
try:
prop.range = [-4.0, 1.0]
self.fail(
"Assigning range not containing current value should not be allowed."
)
except IndexError:
pass # as it should be
try:
prop.clip_on_range = True
self.assertEqual(prop.value, 3.0,
"Value should not have changed yet")
prop.range = [-4.0, 1.0]
self.assertEqual(prop.value, 1.0, "Value should have been clipped")
prop.clip_on_range = False
except IndexError:
pass # as it should be
try:
prop.range = [12]
self.fail("Range should be allowed only if it's a 2-tuple.")
except TypeError:
pass # as it should be
self.assertTrue(self.called == 2)
# "value" update for each change for range change
prop.range = [-4.0, 4.0]
self.assertTrue(self.called == 3)
prop.unsubscribe(self.callback_test_notify)
# Wrong type at init
with self.assertRaises((TypeError, ValueError)):
prop = model.FloatContinuous("Not a number", [-1, 3.4])
# Test a bit the IntContinuous
prop2 = model.IntContinuous(2, [1, 34], unit="px")
self.assertEqual(prop2.value, 2)
self.assertEqual(prop2.range, (1, 34))
prop2.value = 30
self.assertEqual(prop2.value, 30)
self.assertIsInstance(prop2.value, int)
# Wrong type (string) at init
with self.assertRaises((TypeError, ValueError)):
prop2 = model.IntContinuous("Not a number", [-1, 34])
# Wrong type (float) at init
with self.assertRaises((TypeError, ValueError)):
prop2 = model.IntContinuous(3.2, [-1, 34])
def __init__(self, parent, orig_tab_data):
xrcfr_overview_acq.__init__(self, parent)
self.conf = get_acqui_conf()
# True when acquisition occurs
self.acquiring = False
self.data = None
# a ProgressiveFuture if the acquisition is going on
self.acq_future = None
self._acq_future_connector = None
self._main_data_model = orig_tab_data.main
# duplicate the interface, but with only one view
self._tab_data_model = self.duplicate_tab_data_model(orig_tab_data)
# Store the final image as {datelng}-{timelng}-overview
# The pattern to store them in a sub folder, with the name xxxx-overview-tiles/xxx-overview-NxM.ome.tiff
# The pattern to use for storing each tile file individually
# None disables storing them
save_dir = self.conf.last_path
if isinstance(orig_tab_data, guimodel.CryoGUIData):
save_dir = self.conf.pj_last_path
self.filename = create_filename(save_dir,
"{datelng}-{timelng}-overview",
".ome.tiff")
assert self.filename.endswith(".ome.tiff")
dirname, basename = os.path.split(self.filename)
tiles_dir = os.path.join(dirname,
basename[:-len(".ome.tiff")] + "-tiles")
self.filename_tiles = os.path.join(tiles_dir, basename)
# Create a new settings controller for the acquisition dialog
self._settings_controller = LocalizationSettingsController(
self,
self._tab_data_model,
)
self.zsteps = model.IntContinuous(1, range=(1, 51))
# The depth of field is an indication of how far the focus needs to move
# to see the current in-focus position out-of-focus. So it's a good default
# value for the zstep size. We use 2x to "really" see something else.
# Typically, it's about 1 µm.
dof = self._main_data_model.ccd.depthOfField.value
self.zstep_size = model.FloatContinuous(2 * dof,
range=(1e-9, 100e-6),
unit="m")
self._zstep_size_vac = VigilantAttributeConnector(
self.zstep_size, self.zstep_size_ctrl, events=wx.EVT_COMMAND_ENTER)
self.tiles_nx = model.IntContinuous(5, range=(1, 1000))
self.tiles_ny = model.IntContinuous(5, range=(1, 1000))
self._zsteps_vac = VigilantAttributeConnector(self.zsteps,
self.zstack_steps,
events=wx.EVT_SLIDER)
self._tiles_n_vacx = VigilantAttributeConnector(
self.tiles_nx, self.tiles_number_x, events=wx.EVT_COMMAND_ENTER)
self._tiles_n_vacy = VigilantAttributeConnector(
self.tiles_ny, self.tiles_number_y, events=wx.EVT_COMMAND_ENTER)
self.area = None # None or 4 floats: left, top, right, bottom positions of the acquisition area (in m)
orig_view = orig_tab_data.focussedView.value
self._view = self._tab_data_model.focussedView.value
self.streambar_controller = StreamBarController(self._tab_data_model,
self.pnl_secom_streams,
static=True,
ignore_view=True)
# The streams currently displayed are the one visible
self.add_streams()
# The list of streams ready for acquisition (just used as a cache)
self._acq_streams = {}
# Find every setting, and listen to it
self._orig_entries = get_global_settings_entries(
self._settings_controller)
for sc in self.streambar_controller.stream_controllers:
self._orig_entries += get_local_settings_entries(sc)
self.start_listening_to_va()
# make sure the view displays the same thing as the one we are
# duplicating
self._view.view_pos.value = orig_view.view_pos.value
self._view.mpp.value = orig_view.mpp.value
self._view.merge_ratio.value = orig_view.merge_ratio.value
# attach the view to the viewport
self.pnl_view_acq.canvas.fit_view_to_next_image = False
self.pnl_view_acq.setView(self._view, self._tab_data_model)
self.Bind(wx.EVT_CHAR_HOOK, self.on_key)
self.btn_cancel.Bind(wx.EVT_BUTTON, self.on_close)
self.btn_secom_acquire.Bind(wx.EVT_BUTTON, self.on_acquire)
self.Bind(wx.EVT_CLOSE, self.on_close)
# Set parameters for tiled acq
self.overlap = 0.2
try:
# Use the stage range, which can be overridden by the MD_POS_ACTIVE_RANGE.
# Note: this last one might be temporary, until we have a RoA tool provided in the GUI.
self._tiling_rng = {
"x": self._main_data_model.stage.axes["x"].range,
"y": self._main_data_model.stage.axes["y"].range
}
stage_md = self._main_data_model.stage.getMetadata()
if model.MD_POS_ACTIVE_RANGE in stage_md:
self._tiling_rng.update(stage_md[model.MD_POS_ACTIVE_RANGE])
except (KeyError, IndexError):
raise ValueError(
"Failed to find stage.MD_POS_ACTIVE_RANGE with x and y range")
# Note: It should never be possible to reach here with no streams
streams = self.get_acq_streams()
for s in streams:
self._view.addStream(s)
# To update the estimated time & area when streams are removed/added
self._view.stream_tree.flat.subscribe(self.on_streams_changed,
init=True)
def __init__(self, parent, orig_tab_data):
xrcfr_overview_acq.__init__(self, parent)
self.conf = get_acqui_conf()
# True when acquisition occurs
self.acquiring = False
self.data = None
# a ProgressiveFuture if the acquisition is going on
self.acq_future = None
self._acq_future_connector = None
self._main_data_model = orig_tab_data.main
# duplicate the interface, but with only one view
self._tab_data_model = self.duplicate_tab_data_model(orig_tab_data)
# The pattern to use for storing each tile file individually
# None disables storing them
self.filename_tiles = create_filename(self.conf.last_path, "{datelng}-{timelng}-overview",
".ome.tiff")
# Create a new settings controller for the acquisition dialog
self._settings_controller = LocalizationSettingsController(
self,
self._tab_data_model,
highlight_change=True # also adds a "Reset" context menu
)
self.zsteps = model.IntContinuous(1, range=(1, 51))
self._zsteps_vac = VigilantAttributeConnector(self.zsteps, self.zstack_steps, events=wx.EVT_SLIDER)
orig_view = orig_tab_data.focussedView.value
self._view = self._tab_data_model.focussedView.value
self.streambar_controller = StreamBarController(self._tab_data_model,
self.pnl_secom_streams,
static=True,
ignore_view=True)
# The streams currently displayed are the one visible
self.add_streams()
# The list of streams ready for acquisition (just used as a cache)
self._acq_streams = {}
# Compute the preset values for each preset
self._orig_entries = get_global_settings_entries(self._settings_controller)
self._orig_settings = preset_as_is(self._orig_entries)
for sc in self.streambar_controller.stream_controllers:
self._orig_entries += get_local_settings_entries(sc)
self.start_listening_to_va()
# make sure the view displays the same thing as the one we are
# duplicating
self._view.view_pos.value = orig_view.view_pos.value
self._view.mpp.value = orig_view.mpp.value
self._view.merge_ratio.value = orig_view.merge_ratio.value
# attach the view to the viewport
self.pnl_view_acq.canvas.fit_view_to_next_image = False
self.pnl_view_acq.setView(self._view, self._tab_data_model)
self.Bind(wx.EVT_CHAR_HOOK, self.on_key)
self.btn_cancel.Bind(wx.EVT_BUTTON, self.on_close)
self.btn_secom_acquire.Bind(wx.EVT_BUTTON, self.on_acquire)
self.Bind(wx.EVT_CLOSE, self.on_close)
# on_streams_changed is compatible because it doesn't use the args
# To update the estimated time when streams are removed/added
self._view.stream_tree.flat.subscribe(self.on_streams_changed)
# Set parameters for tiled acq
self.overlap = 0.2
try:
# Use the stage range, which can be overridden by the MD_POS_ACTIVE_RANGE,
# which can be overridden by MD_OVERVIEW_RANGE.
# Note: this last one might be temporary, until we have a RoA tool provided in the GUI.
stage_rng = {
"x": self._main_data_model.stage.axes["x"].range,
"y": self._main_data_model.stage.axes["y"].range
}
stage_md = self._main_data_model.stage.getMetadata()
if model.MD_POS_ACTIVE_RANGE in stage_md:
stage_rng.update(stage_md[model.MD_POS_ACTIVE_RANGE])
if model.MD_OVERVIEW_RANGE in stage_md:
stage_rng.update(stage_md[model.MD_OVERVIEW_RANGE])
# left, bottom, right, top
self.area = (stage_rng["x"][0], stage_rng["y"][0], stage_rng["x"][1], stage_rng["y"][1])
except (KeyError, IndexError):
raise ValueError("Failed to find stage.MD_POS_ACTIVE_RANGE with x and y range")
# Note: It should never be possible to reach here with no streams
streams = self.get_acq_streams()
for s in streams:
self._view.addStream(s)
self.update_acquisition_time()
def __init__(self, name, detector, sed, emitter, opm=None):
"""
name (string): user-friendly name of this stream
detector (Detector): the monochromator
sed (Detector): the se-detector
emitter (Emitter): the emitter (eg: ebeam scanner)
spectrograph (Actuator): the spectrograph
"""
self.name = model.StringVA(name)
# Hardware Components, detector is the correlator, sed is the secondary electron image and the emitter is the electron beam
self._detector = detector
self._sed = sed
self._emitter = emitter
self._opm = opm
self.is_active = model.BooleanVA(False)
#dwell time and exposure time are the same thing in this case
self.dwellTime = model.FloatContinuous(
1, range=self._emitter.dwellTime.range, unit="s")
# pixelDuration of correlator, this can be shortened once implemented as choices.
self.pixelDuration = model.FloatEnumerated(
512e-12,
choices={
4e-12, 8e-12, 16e-12, 32e-12, 64e-12, 128e-12, 256e-12, 512e-12
},
unit="s",
)
#Sync Offset time correlator
self.syncOffset = self._detector.syncOffset
#Sync Divider time correlator
self.syncDiv = self._detector.syncDiv
# Distance between the center of each pixel
self.stepsize = model.FloatContinuous(1e-6, (1e-9, 1e-4), unit="m")
# Region of acquisition. ROI form is LEFT Top RIGHT Bottom, relative to full field size
self.roi = model.TupleContinuous((0, 0, 1, 1),
range=((0, 0, 0, 0), (1, 1, 1, 1)),
cls=(int, long, float))
# Cropvalue that can be used to crop the data for better visualization in odemis
self.cropvalue = model.IntContinuous(1024, (1, 65536), unit="px")
# For drift correction
self.dcRegion = model.TupleContinuous(UNDEFINED_ROI,
range=((0, 0, 0, 0), (1, 1, 1,
1)),
cls=(int, long, float))
self.dcDwellTime = model.FloatContinuous(emitter.dwellTime.range[0],
range=emitter.dwellTime.range,
unit="s")
#number of drift corrections per scanning pixel
self.nDC = model.IntContinuous(1, (1, 20))
# For acquisition
self.tc_data = None
self.tc_data_received = threading.Event()
self.sem_data = []
self.sem_data_received = threading.Event()
self._hw_settings = None
def __init__(self, microscope, main_app):
super(SRAcqPlugin, self).__init__(microscope, main_app)
# Can only be used with a microscope
if not microscope:
return
# Check if the microscope is a SECOM
main_data = self.main_app.main_data
if not main_data.ccd or not main_data.light:
return
self.light = main_data.light
self.ccd = main_data.ccd
self.addMenu("Acquisition/Super-resolution...", self.start)
# Add the useful VAs which are available on the CCD.
# (on an iXon, they should all be there)
for n in ("exposureTime", "resolution", "binning", "gain", "emGain",
"countConvertWavelength", "temperature",
"readoutRate", "verticalReadoutRate", "verticalClockVoltage"):
if model.hasVA(self.ccd, n):
va = getattr(self.ccd, n)
setattr(self, n, va)
# Trick to pass the component (ccd to binning_1d_from_2d())
self.vaconf["binning"]["choices"] = (lambda cp, va, cf:
gui.conf.util.binning_1d_from_2d(self.ccd, va, cf))
self.vaconf["resolution"]["choices"] = (lambda cp, va, cf:
gui.conf.util.resolution_from_range(self.ccd, va, cf))
self.number = model.IntContinuous(1000, (1, 1000000))
self.filename = model.StringVA("a.tiff")
self.filename.subscribe(self._on_filename)
self.expectedDuration = model.VigilantAttribute(1, unit="s", readonly=True)
self.number.subscribe(self._update_exp_dur)
self.exposureTime.subscribe(self._update_exp_dur)
# Create a stream to show the settings changes
self._stream = stream.FluoStream(
"Filtered colour",
self.ccd,
self.ccd.data,
emitter=main_data.light,
em_filter=main_data.light_filter,
focuser=main_data.focus,
)
# For the acquisition
self._acq_done = threading.Event()
self._n = 0
self._startt = 0 # starting time of acquisition
self._last_display = 0 # last time the GUI image was updated
self._future = None # future to represent the acquisition progress
self._exporter = None # to save the file
self._q = queue.Queue() # queue of tuples (str, DataArray) for saving data
self._qdisplay = queue.Queue()
# 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 __init__(self,
name,
detector,
sed,
emitter,
spectrograph,
lens_switch,
bigslit,
opm,
wl_inverted=False):
"""
name (string): user-friendly name of this stream
detector (Detector): the 2D CCD which get wavelength on the X axis and angles on the Y axis
sed (Detector): the se-detector
emitter (Emitter): the emitter (eg: ebeam scanner)
spectrograph (Actuator): the spectrograph
wl_inverted (bool): if True, will swap the wavelength axis of the CCD, in
order to support hardware where the highest wavelengths are at the smallest
indices. (The MD_WL_LIST is *not* inverted)
"""
self.name = model.StringVA(name)
# Hardware Components
self._detector = detector
self._sed = sed
self._emitter = emitter
self._sgr = spectrograph
self._opm = opm
self._lsw = lens_switch
self._bigslit = bigslit
self._wl_inverted = wl_inverted
wlr = spectrograph.axes["wavelength"].range
slitw = spectrograph.axes["slit-in"].range
self.centerWavelength = model.FloatContinuous(500e-9, wlr, unit="m")
self.slitWidth = model.FloatContinuous(100e-6, slitw, unit="m")
# dwell time and exposure time are the same thing in this case
self.dwellTime = model.FloatContinuous(
1, range=detector.exposureTime.range, unit="s")
self.emtTranslation = model.TupleContinuous(
(0, 0),
range=self._emitter.translation.range,
cls=(int, long, float),
unit="px")
# Distance between the center of each pixel
self.stepsize = model.FloatContinuous(1e-6, (1e-9, 1e-4), unit="m")
# Region of acquisition. ROI form is LEFT Top RIGHT Bottom, relative to full field size
self.roi = model.TupleContinuous((0, 0, 1, 1),
range=((0, 0, 0, 0), (1, 1, 1, 1)),
cls=(int, long, float))
# For drift correction
self.dcRegion = model.TupleContinuous(UNDEFINED_ROI,
range=((0, 0, 0, 0), (1, 1, 1,
1)),
cls=(int, long, float))
self.dcDwellTime = model.FloatContinuous(emitter.dwellTime.range[0],
range=emitter.dwellTime.range,
unit="s")
#self.binning = model.VAEnumerated((1,1), choices=set([(1,1), (2,2), (2,3)]))
# separate binning values because it can useful for experiment
self.binninghorz = model.VAEnumerated(1, choices={1, 2, 4, 8, 16})
self.binningvert = model.VAEnumerated(1, choices={1, 2, 4, 8, 16})
self.nDC = model.IntContinuous(1, (1, 20))
# For acquisition
self.ARspectral_data = None
self.ARspectral_data_received = threading.Event()
self.sem_data = []
self.sem_data_received = threading.Event()
self._hw_settings = None
def __init__(self, name, image):
"""
name (string)
image (model.DataArray of shape (CYX) or (C11YX)). The metadata
MD_WL_POLYNOMIAL should be included in order to associate the C to a
wavelength.
"""
self._calibrated = None # just for the _updateDRange to not complain
Stream.__init__(self, name, None, None, None)
# Spectrum stream has in addition to normal stream:
# * information about the current bandwidth displayed (avg. spectrum)
# * coordinates of 1st point (1-point, line)
# * coordinates of 2nd point (line)
if len(image.shape) == 3:
# force 5D
image = image[:, numpy.newaxis, numpy.newaxis, :, :]
elif len(image.shape) != 5 or image.shape[1:3] != (1, 1):
logging.error("Cannot handle data of shape %s", image.shape)
raise NotImplementedError("SpectrumStream needs a cube data")
# ## this is for "average spectrum" projection
try:
# cached list of wavelength for each pixel pos
self._wl_px_values = spectrum.get_wavelength_per_pixel(image)
except (ValueError, KeyError):
# useless polynomial => just show pixels values (ex: -50 -> +50 px)
# TODO: try to make them always int?
max_bw = image.shape[0] // 2
min_bw = (max_bw - image.shape[0]) + 1
self._wl_px_values = range(min_bw, max_bw + 1)
assert (len(self._wl_px_values) == image.shape[0])
unit_bw = "px"
cwl = (max_bw + min_bw) // 2
width = image.shape[0] // 12
else:
min_bw, max_bw = self._wl_px_values[0], self._wl_px_values[-1]
unit_bw = "m"
cwl = (max_bw + min_bw) / 2
width = (max_bw - min_bw) / 12
# TODO: allow to pass the calibration data as argument to avoid
# recomputing the data just after init?
# Spectrum efficiency compensation data: None or a DataArray (cf acq.calibration)
self.efficiencyCompensation = model.VigilantAttribute(
None, setter=self._setEffComp)
# The background data (typically, an acquisition without ebeam).
# It is subtracted from the acquisition data.
# If set to None, a simple baseline background value is subtracted.
self.background = model.VigilantAttribute(None,
setter=self._setBackground)
# low/high values of the spectrum displayed
self.spectrumBandwidth = model.TupleContinuous(
(cwl - width, cwl + width),
range=((min_bw, min_bw), (max_bw, max_bw)),
unit=unit_bw,
cls=(int, long, float))
# Whether the (per bandwidth) display should be split intro 3 sub-bands
# which are applied to RGB
self.fitToRGB = model.BooleanVA(False)
self._drange = None
# This attribute is used to keep track of any selected pixel within the
# data for the display of a spectrum
self.selected_pixel = model.TupleVA((None, None)) # int, int
# first point, second point in pixels. It must be 2 elements long.
self.selected_line = model.ListVA([(None, None), (None, None)],
setter=self._setLine)
# The thickness of a point of a line (shared).
# A point of width W leads to the average value between all the pixels
# which are within W/2 from the center of the point.
# A line of width W leads to a 1D spectrum taking into account all the
# pixels which fit on an orthogonal line to the selected line at a
# distance <= W/2.
self.width = model.IntContinuous(1, [1, 50], unit="px")
self.fitToRGB.subscribe(self.onFitToRGB)
self.spectrumBandwidth.subscribe(self.onSpectrumBandwidth)
self.efficiencyCompensation.subscribe(self._onCalib)
self.background.subscribe(self._onCalib)
self.raw = [image
] # for compatibility with other streams (like saving...)
self._calibrated = image # the raw data after calibration
self._updateDRange()
self._updateHistogram()
self._updateImage()
def __init__(self, name, image, *args, **kwargs):
"""
name (string)
image (model.DataArray(Shadow) of shape (CYX), (C11YX), (CTYX), (CT1YX), (1T1YX)).
The metadata MD_WL_POLYNOMIAL or MD_WL_LIST should be included in order to
associate the C to a wavelength.
The metadata MD_TIME_LIST should be included to associate the T to a timestamp
.background is a DataArray of shape (CT111), where C & T have the same length as in the data.
.efficiencyCompensation is always DataArray of shape C1111.
"""
# Spectrum stream has in addition to normal stream:
# * information about the current bandwidth displayed (avg. spectrum)
# * coordinates of 1st point (1-point, line)
# * coordinates of 2nd point (line)
# TODO: need to handle DAS properly, in case it's tiled (in XY), to avoid
# loading too much data in memory.
# Ensure the data is a DataArray, as we don't handle (yet) DAS
if isinstance(image, model.DataArrayShadow):
image = image.getData()
if len(image.shape) == 3:
# force 5D for CYX
image = image[:, numpy.newaxis, numpy.newaxis, :, :]
elif len(image.shape) == 4:
# force 5D for CTYX
image = image[:, :, numpy.newaxis, :, :]
elif len(image.shape) != 5 or image.shape[2] != 1:
logging.error("Cannot handle data of shape %s", image.shape)
raise NotImplementedError(
"StaticSpectrumStream needs 3D or 4D data")
# This is for "average spectrum" projection
# cached list of wavelength for each pixel pos
self._wl_px_values, unit_bw = spectrum.get_spectrum_range(image)
min_bw, max_bw = self._wl_px_values[0], self._wl_px_values[-1]
cwl = (max_bw + min_bw) / 2
width = (max_bw - min_bw) / 12
# The selected wavelength for a temporal spectrum display
self.selected_wavelength = model.FloatContinuous(
self._wl_px_values[0],
range=(min_bw, max_bw),
unit=unit_bw,
setter=self._setWavelength)
# Is there time data?
if image.shape[1] > 1:
# cached list of timestamps for each position in the time dimension
self._tl_px_values, unit_t = spectrum.get_time_range(image)
min_t, max_t = self._tl_px_values[0], self._tl_px_values[-1]
# Allow the select the time as any value within the range, and the
# setter will automatically "snap" it to the closest existing timestamp
self.selected_time = model.FloatContinuous(self._tl_px_values[0],
range=(min_t, max_t),
unit=unit_t,
setter=self._setTime)
# This attribute is used to keep track of any selected pixel within the
# data for the display of a spectrum
self.selected_pixel = model.TupleVA((None, None)) # int, int
# first point, second point in pixels. It must be 2 elements long.
self.selected_line = model.ListVA([(None, None), (None, None)],
setter=self._setLine)
# The thickness of a point or a line (shared).
# A point of width W leads to the average value between all the pixels
# which are within W/2 from the center of the point.
# A line of width W leads to a 1D spectrum taking into account all the
# pixels which fit on an orthogonal line to the selected line at a
# distance <= W/2.
self.selectionWidth = model.IntContinuous(1, [1, 50], unit="px")
self.selectionWidth.subscribe(self._onSelectionWidth)
# Peak method index, None if spectrum peak fitting curve is not displayed
self.peak_method = model.VAEnumerated("gaussian",
{"gaussian", "lorentzian", None})
# TODO: allow to pass the calibration data as argument to avoid
# recomputing the data just after init?
# Spectrum efficiency compensation data: None or a DataArray (cf acq.calibration)
self.efficiencyCompensation = model.VigilantAttribute(
None, setter=self._setEffComp)
self.efficiencyCompensation.subscribe(self._onCalib)
# Is there spectrum data?
if image.shape[0] > 1:
# low/high values of the spectrum displayed
self.spectrumBandwidth = model.TupleContinuous(
(cwl - width, cwl + width),
range=((min_bw, min_bw), (max_bw, max_bw)),
unit=unit_bw,
cls=(int, long, float))
self.spectrumBandwidth.subscribe(self.onSpectrumBandwidth)
# Whether the (per bandwidth) display should be split intro 3 sub-bands
# which are applied to RGB
self.fitToRGB = model.BooleanVA(False)
self.fitToRGB.subscribe(self.onFitToRGB)
# the raw data after calibration
self.calibrated = model.VigilantAttribute(image)
if "acq_type" not in kwargs:
if image.shape[0] > 1 and image.shape[1] > 1:
kwargs["acq_type"] = model.MD_AT_TEMPSPECTRUM
elif image.shape[0] > 1:
kwargs["acq_type"] = model.MD_AT_SPECTRUM
elif image.shape[1] > 1:
kwargs["acq_type"] = model.MD_AT_TEMPORAL
else:
logging.warning(
"SpectrumStream data has no spectrum or time dimension, shape = %s",
image.shape)
super(StaticSpectrumStream, self).__init__(name, [image], *args,
**kwargs)
# Automatically select point/line if data is small (can only be done
# after .raw is set)
if image.shape[-2:] == (1,
1): # Only one point => select it immediately
self.selected_pixel.value = (0, 0)
elif image.shape[
-2] == 1: # Horizontal line => select line immediately
self.selected_line.value = [(0, 0), (image.shape[-1] - 1, 0)]
elif image.shape[-1] == 1: # Vertical line => select line immediately
self.selected_line.value = [(0, 0), (0, image.shape[-2] - 1)]
def __init__(self, name, raw, *args, **kwargs):
"""
Note: parameters are different from the base class.
raw (DataArray or DataArrayShadow): The data to display.
"""
# if raw is a DataArrayShadow, but not pyramidal, read the data to a DataArray
if isinstance(raw,
model.DataArrayShadow) and not hasattr(raw, 'maxzoom'):
raw = [raw.getData()]
else:
raw = [raw]
metadata = copy.copy(raw[0].metadata)
# If there are 5 dims in CTZYX, eliminate CT and only take spatial dimensions
if raw[0].ndim >= 3:
dims = metadata.get(model.MD_DIMS, "CTZYX"[-raw[0].ndim::])
if dims[-3:] != "ZYX":
logging.warning(
"Metadata has %s dimensions, which may be invalid.", dims)
if len(raw[0].shape) == 5:
if any(x > 1 for x in raw[0].shape[:2]):
logging.error(
"Higher dimensional data is being discarded.")
raw[0] = raw[0][0, 0]
elif len(raw[0].shape) == 4:
if any(x > 1 for x in raw[0].shape[:1]):
logging.error(
"Higher dimensional data is being discarded.")
raw[0] = raw[0][0]
# Squash the Z dimension if it's empty
if raw[0].shape[0] == 1:
raw[0] = raw[0][0, :, :]
metadata[model.MD_DIMS] = "CTZYX"[-raw[0].ndim::]
# Define if z-index should be created.
if len(raw[0].shape) == 3 and metadata[model.MD_DIMS] == "ZYX":
try:
pxs = metadata[model.MD_PIXEL_SIZE]
pos = metadata[model.MD_POS]
if len(pxs) < 3:
assert len(pxs) == 2
logging.warning(
u"Metadata for 3D data invalid. Using default pixel size 10µm"
)
pxs = (pxs[0], pxs[1], 10e-6)
metadata[model.MD_PIXEL_SIZE] = pxs
if len(pos) < 3:
assert len(pos) == 2
pos = (pos[0], pos[1], 0)
metadata[model.MD_POS] = pos
logging.warning(
u"Metadata for 3D data invalid. Using default centre position 0"
)
except KeyError:
raise ValueError(
"Pixel size or position are missing from metadata")
# Define a z-index
self.zIndex = model.IntContinuous(0, (0, raw[0].shape[0] - 1))
self.zIndex.subscribe(self._on_zIndex)
# Copy back the metadata
raw[0].metadata = metadata
super(Static2DStream, self).__init__(name, raw, *args, **kwargs)
def __init__(self, name, image):
"""
name (string)
image (model.DataArray(Shadow) of shape (CYX) or (C11YX)). The metadata
MD_WL_POLYNOMIAL or MD_WL_LIST should be included in order to associate the C to a
wavelength.
"""
# Spectrum stream has in addition to normal stream:
# * information about the current bandwidth displayed (avg. spectrum)
# * coordinates of 1st point (1-point, line)
# * coordinates of 2nd point (line)
# TODO: need to handle DAS properly, in case it's tiled (in XY), to avoid
# loading too much data in memory.
# Ensure the data is a DataArray, as we don't handle (yet) DAS
if isinstance(image, model.DataArrayShadow):
image = image.getData()
if len(image.shape) == 3:
# force 5D
image = image[:, numpy.newaxis, numpy.newaxis, :, :]
elif len(image.shape) != 5 or image.shape[1:3] != (1, 1):
logging.error("Cannot handle data of shape %s", image.shape)
raise NotImplementedError("SpectrumStream needs a cube data")
# This is for "average spectrum" projection
try:
# cached list of wavelength for each pixel pos
self._wl_px_values = spectrum.get_wavelength_per_pixel(image)
except (ValueError, KeyError):
# useless polynomial => just show pixels values (ex: -50 -> +50 px)
# TODO: try to make them always int?
max_bw = image.shape[0] // 2
min_bw = (max_bw - image.shape[0]) + 1
self._wl_px_values = range(min_bw, max_bw + 1)
assert(len(self._wl_px_values) == image.shape[0])
unit_bw = "px"
cwl = (max_bw + min_bw) // 2
width = image.shape[0] // 12
else:
min_bw, max_bw = self._wl_px_values[0], self._wl_px_values[-1]
unit_bw = "m"
cwl = (max_bw + min_bw) / 2
width = (max_bw - min_bw) / 12
# TODO: allow to pass the calibration data as argument to avoid
# recomputing the data just after init?
# Spectrum efficiency compensation data: None or a DataArray (cf acq.calibration)
self.efficiencyCompensation = model.VigilantAttribute(None, setter=self._setEffComp)
# The background data (typically, an acquisition without e-beam).
# It is subtracted from the acquisition data.
# If set to None, a simple baseline background value is subtracted.
self.background = model.VigilantAttribute(None, setter=self._setBackground)
# low/high values of the spectrum displayed
self.spectrumBandwidth = model.TupleContinuous(
(cwl - width, cwl + width),
range=((min_bw, min_bw), (max_bw, max_bw)),
unit=unit_bw,
cls=(int, long, float))
# Whether the (per bandwidth) display should be split intro 3 sub-bands
# which are applied to RGB
self.fitToRGB = model.BooleanVA(False)
# This attribute is used to keep track of any selected pixel within the
# data for the display of a spectrum
self.selected_pixel = model.TupleVA((None, None)) # int, int
# first point, second point in pixels. It must be 2 elements long.
self.selected_line = model.ListVA([(None, None), (None, None)], setter=self._setLine)
# Peak method index, None if spectrum peak fitting curve is not displayed
self.peak_method = model.VAEnumerated("gaussian", {"gaussian", "lorentzian", None})
# The thickness of a point or a line (shared).
# A point of width W leads to the average value between all the pixels
# which are within W/2 from the center of the point.
# A line of width W leads to a 1D spectrum taking into account all the
# pixels which fit on an orthogonal line to the selected line at a
# distance <= W/2.
self.selectionWidth = model.IntContinuous(1, [1, 50], unit="px")
self.fitToRGB.subscribe(self.onFitToRGB)
self.spectrumBandwidth.subscribe(self.onSpectrumBandwidth)
self.efficiencyCompensation.subscribe(self._onCalib)
self.background.subscribe(self._onCalib)
self.selectionWidth.subscribe(self._onSelectionWidth)
self._calibrated = image # the raw data after calibration
super(StaticSpectrumStream, self).__init__(name, [image])
# Automatically select point/line if data is small (can only be done
# after .raw is set)
if image.shape[-2:] == (1, 1): # Only one point => select it immediately
self.selected_pixel.value = (0, 0)
elif image.shape[-2] == 1: # Horizontal line => select line immediately
self.selected_line.value = [(0, 0), (image.shape[-1] - 1, 0)]
elif image.shape[-1] == 1: # Vertical line => select line immediately
self.selected_line.value = [(0, 0), (0, image.shape[-2] - 1)]