def test_spectrum_line_select_overlay(self):
cnvs = miccanvas.DblMicroscopeCanvas(self.panel)
tab_mod = self.create_simple_tab_model()
view = tab_mod.focussedView.value
self.add_control(cnvs, wx.EXPAND, proportion=1, clear=True)
cnvs.setView(view, tab_mod)
cnvs.current_mode = TOOL_POINT
slol = wol.SpectrumLineSelectOverlay(cnvs)
slol.activate()
cnvs.add_world_overlay(slol)
slol.set_data_properties(1e-05, (0.0, 0.0), (17, 19))
width_va = model.IntVA(1)
line_va = model.TupleVA(((None, None), (None, None)))
slol.connect_selection(line_va, width_va)
view.mpp.value = 1e-06
test.gui_loop()
# Tool toggle for debugging
tol = vol.TextViewOverlay(cnvs)
tol.add_label("Right click to toggle tool", (10, 30))
cnvs.add_view_overlay(tol)
test.gui_loop()
line_va.value = ((0, 0), (8, 8))
test.gui_loop()
# Also connect the pixel va
pixel_va = model.TupleVA((8, 8))
slol.connect_selection(line_va, width_va, pixel_va)
test.gui_loop()
def toggle(evt):
if slol.active:
slol.deactivate()
else:
slol.activate()
evt.Skip()
cnvs.Bind(wx.EVT_RIGHT_UP, toggle)
cnvs.disable_drag()
def on_key(evt):
k = evt.GetKeyCode()
if k == wx.WXK_DOWN and width_va.value > 1:
width_va.value -= 1
elif k == wx.WXK_UP:
width_va.value += 1
else:
pass
cnvs.Bind(wx.EVT_KEY_UP, on_key)
def test_spot_mode_world_overlay(self):
sem = simsem.SimSEM(**CONFIG_SEM)
for child in sem.children.value:
if child.name == CONFIG_SCANNER["name"]:
ebeam = child
# Simulate a stage move
ebeam.updateMetadata({model.MD_POS: (1e-3, -0.2e-3)})
cnvs = miccanvas.DblMicroscopeCanvas(self.panel)
cnvs.background_brush = wx.BRUSHSTYLE_CROSS_HATCH
self.add_control(cnvs, wx.EXPAND, proportion=1, clear=True)
spotPosition = model.TupleVA((0.1, 0.1))
sol = wol.SpotModeOverlay(cnvs, spot_va=spotPosition, scanner=ebeam)
sol.activate()
cnvs.add_world_overlay(sol)
cnvs.scale = 100000
cnvs.update_drawing()
test.gui_loop(1)
spotPosition.value = (0.5, 0.5)
test.gui_loop(1)
spotPosition.value = (None, None)
test.gui_loop()
self.assertIsNone(sol.p_pos, None)
def test_roa_select_overlay_va(self):
sem = simsem.SimSEM(**CONFIG_SEM)
for child in sem.children.value:
if child.name == CONFIG_SCANNER["name"]:
ebeam = child
# Simulate a stage move
ebeam.updateMetadata({model.MD_POS: (1e-3, -0.2e-3)})
# but it should be a simple miccanvas
cnvs = miccanvas.DblMicroscopeCanvas(self.panel)
self.add_control(cnvs, wx.EXPAND, proportion=1, clear=True)
roa = model.TupleVA(UNDEFINED_ROI)
rsol = wol.RepetitionSelectOverlay(cnvs, roa=roa, scanner=ebeam)
rsol.activate()
cnvs.add_world_overlay(rsol)
cnvs.scale = 100000
cnvs.update_drawing()
# Undefined ROA => sel = None
roi_back = rsol.get_physical_sel()
self.assertEqual(roi_back, None)
# Full FoV
roa.value = (0, 0, 1, 1)
test.gui_loop(0.1)
# Expect the whole SEM FoV
fov = compute_scanner_fov(ebeam)
ebeam_rect = get_fov_rect(ebeam, fov)
roi_back = rsol.get_physical_sel()
for o, b in zip(ebeam_rect, roi_back):
self.assertAlmostEqual(o,
b,
msg="ebeam FoV (%s) != ROI (%s)" %
(ebeam_rect, roi_back))
# Hald the FoV
roa.value = (0.25, 0.25, 0.75, 0.75)
test.gui_loop(0.1)
# Expect the whole SEM FoV
fov = compute_scanner_fov(ebeam)
fov = (fov[0] / 2, fov[1] / 2)
ebeam_rect = get_fov_rect(ebeam, fov)
roi_back = rsol.get_physical_sel()
for o, b in zip(ebeam_rect, roi_back):
self.assertAlmostEqual(o,
b,
msg="ebeam FoV (%s) != ROI (%s)" %
(ebeam_rect, roi_back))
test.gui_loop()
sem.terminate()
def test_pixel_select_overlay(self):
cnvs = miccanvas.DblMicroscopeCanvas(self.panel)
tab_mod = self.create_simple_tab_model()
view = tab_mod.focussedView.value
self.add_control(cnvs, wx.EXPAND, proportion=1, clear=True)
# FIXME: when setView is called *before* the add_control, the picture goes black and no
# pixels are visible
cnvs.setView(view, tab_mod)
cnvs.current_mode = TOOL_POINT
psol = wol.PixelSelectOverlay(cnvs)
psol.activate()
psol.enabled = True
cnvs.add_world_overlay(psol)
psol.set_data_properties(1e-05, (0.0, 0.0), (17, 19))
width_va = model.IntVA(1)
psol.connect_selection(model.TupleVA(), width_va)
view.mpp.value = 1e-06
psol._selected_pixel_va.value = (8, 8)
test.gui_loop()
# Tool toggle for debugging
tol = vol.TextViewOverlay(cnvs)
tol.add_label("Right click to toggle tool", (10, 30))
cnvs.add_view_overlay(tol)
def toggle(evt):
if psol.active:
psol.deactivate()
else:
psol.activate()
evt.Skip()
cnvs.Bind(wx.EVT_RIGHT_UP, toggle)
cnvs.disable_drag()
def on_key(evt):
k = evt.GetKeyCode()
if k == wx.WXK_DOWN and width_va.value > 1:
width_va.value -= 1
elif k == wx.WXK_UP:
width_va.value += 1
else:
pass
cnvs.Bind(wx.EVT_KEY_UP, on_key)
def __init__(self, name, role, positions, has_pressure=False, **kwargs):
"""
Initialises the component
positions (list of str): each pressure positions supported by the
component (among the allowed ones)
has_pressure (boolean): if True, has a pressure VA with the current
pressure.
"""
super(PhenomChamber, self).__init__(name, role, positions, has_pressure, **kwargs)
# sample holder VA is a read-only tuple with holder ID/type
# TODO: set to None/None when the sample is ejected
self.sampleHolder = model.TupleVA((PHENOM_SH_FAKE_ID, PHENOM_SH_TYPE_OPTICAL),
readonly=True)
def __init__(self, microscope, main_app):
super(TileAcqPlugin, self).__init__(microscope, main_app)
self._dlg = None
self._tab = None # the acquisition tab
self.ft = model.InstantaneousFuture() # acquisition future
self.microscope = microscope
# Can only be used with a microscope
if not microscope:
return
else:
# Check if microscope supports tiling (= has a sample stage)
main_data = self.main_app.main_data
if main_data.stage:
self.addMenu("Acquisition/Tile...\tCtrl+G", self.show_dlg)
else:
logging.info(
"Tile acquisition not available as no stage present")
return
self._ovrl_stream = None # stream for fine alignment
self.nx = model.IntContinuous(5, (1, 1000), setter=self._set_nx)
self.ny = model.IntContinuous(5, (1, 1000), setter=self._set_ny)
self.overlap = model.FloatContinuous(20, (-80, 80), unit="%")
self.angle = model.FloatContinuous(0, (-90, 90), unit=u"°")
self.filename = model.StringVA("a.ome.tiff")
self.expectedDuration = model.VigilantAttribute(1,
unit="s",
readonly=True)
self.totalArea = model.TupleVA((1, 1), unit="m", readonly=True)
self.stitch = model.BooleanVA(True)
self.fineAlign = model.BooleanVA(False)
# TODO: manage focus (eg, autofocus or ask to manual focus on the corners
# of the ROI and linearly interpolate)
self.nx.subscribe(self._update_exp_dur)
self.ny.subscribe(self._update_exp_dur)
self.fineAlign.subscribe(self._update_exp_dur)
self.nx.subscribe(self._update_total_area)
self.ny.subscribe(self._update_total_area)
self.overlap.subscribe(self._update_total_area)
# Warn if memory will be exhausted
self.nx.subscribe(self._memory_check)
self.ny.subscribe(self._memory_check)
self.stitch.subscribe(self._memory_check)
def test_tuple(self):
"""
Tuple VA
"""
va = model.TupleVA((0.1, 10, .5))
self.assertEqual(va.value, (0.1, 10, .5))
# change value
va.value = (-0.2, 2, .2)
self.assertEqual(va.value, (-0.2, 2, .2))
# check None is possible as value
# TODO remove this functionality? Does not look like a good idea to allow None on a tuple VA
va.value = None
self.assertIsNone(va.value)
# must convert list to a tuple
va.value = [-1, 150, .5]
self.assertEqual(va.value, (-1, 150, .5))
def test_pixel_select_overlay(self):
cnvs = miccanvas.DblMicroscopeCanvas(self.panel)
tab_mod = self.create_simple_tab_model()
view = tab_mod.focussedView.value
self.add_control(cnvs, wx.EXPAND, proportion=1, clear=True)
# FIXME: when setView is called *before* the add_control, the picture goes black and no
# pixels are visible
cnvs.setView(view, tab_mod)
cnvs.current_mode = TOOL_POINT
psol = wol.PixelSelectOverlay(cnvs)
psol.activate()
psol.enabled = True
cnvs.add_world_overlay(psol)
# psol.set_values(33, (0.0, 0.0), (30, 30))
psol.set_values(1e-05, (0.0, 0.0), (17, 19), omodel.TupleVA())
view.mpp.value = 1e-06
test.gui_loop()
# Tool toggle for debugging
tol = vol.TextViewOverlay(cnvs)
tol.add_label("Right click to toggle tool", (10, 30))
cnvs.add_view_overlay(tol)
def toggle(evt):
if psol.active:
psol.deactivate()
else:
psol.activate()
evt.Skip()
cnvs.Bind(wx.EVT_RIGHT_UP, toggle)
def __init__(self, microscope, main_app):
super(ARspectral, self).__init__(microscope, main_app)
# Can only be used on a Sparc with a CCD
if not microscope:
return
main_data = self.main_app.main_data
self.ebeam = main_data.ebeam
self.ccd = main_data.ccd
self.sed = main_data.sed
self.sgrh = main_data.spectrograph
if not all((self.ebeam, self.ccd, self.sed, self.sgrh)):
logging.debug("Hardware not found, cannot use the plugin")
return
# TODO: handle SPARC systems which don't have such hardware
bigslit = model.getComponent(role="slit-in-big")
lsw = model.getComponent(role="lens-switch")
# This is a little tricky: we don't directly need the spectrometer, the
# 1D image of the CCD, as we are interested in the raw image. However,
# we care about the wavelengths and the spectrometer might be inverted
# in order to make sure the wavelength is is the correct direction (ie,
# lowest pixel = lowest wavelength). So we need to do the same on the
# raw image. However, there is no "official" way to connect the
# spectrometer(s) to their raw CCD. So we rely on the fact that
# typically this is a wrapper, so we can check using the .dependencies.
wl_inverted = False
try:
spec = self._find_spectrometer(self.ccd)
except LookupError as ex:
logging.warning("%s, expect that the wavelengths are not inverted",
ex)
else:
# Found spec => check transpose in X (1 or -1), and invert if it's inverted (-1)
try:
wl_inverted = (spec.transpose[0] == -1)
except Exception as ex:
# Just in case spec has no .transpose or it's not a tuple
# (very unlikely as all Detectors have it)
logging.warning(
"%s: expect that the wavelengths are not inverted", ex)
# the SEM survey stream (will be updated when showing the window)
self._survey_s = None
# Create a stream for AR spectral measurement
self._ARspectral_s = SpectralARScanStream("AR Spectrum", self.ccd,
self.sed, self.ebeam,
self.sgrh, lsw, bigslit,
main_data.opm, wl_inverted)
# For reading the ROA and anchor ROI
self._tab = main_data.getTabByName("sparc_acqui")
self._tab_data = self._tab.tab_data_model
# The settings to be displayed in the dialog
# Trick: we use the same VAs as the stream, so they are directly synchronised
self.centerWavelength = self._ARspectral_s.centerWavelength
#self.numberOfPixels = self._ARspectral_s.numberOfPixels
self.dwellTime = self._ARspectral_s.dwellTime
self.slitWidth = self._ARspectral_s.slitWidth
self.binninghorz = self._ARspectral_s.binninghorz
self.binningvert = self._ARspectral_s.binningvert
self.nDC = self._ARspectral_s.nDC
self.grating = model.IntEnumerated(
self.sgrh.position.value["grating"],
choices=self.sgrh.axes["grating"].choices,
setter=self._onGrating)
self.roi = self._ARspectral_s.roi
self.stepsize = self._ARspectral_s.stepsize
self.res = model.TupleVA((1, 1), unit="px")
self.cam_res = model.TupleVA((self.ccd.shape[0], self.ccd.shape[1]),
unit="px")
self.gain = self.ccd.gain
self.readoutRate = self.ccd.readoutRate
self.filename = model.StringVA("a.h5")
self.expectedDuration = model.VigilantAttribute(1,
unit="s",
readonly=True)
# Update the expected duration when values change, depends both dwell time and # of pixels
self.dwellTime.subscribe(self._update_exp_dur)
self.stepsize.subscribe(self._update_exp_dur)
self.nDC.subscribe(self._update_exp_dur)
self.readoutRate.subscribe(self._update_exp_dur)
self.cam_res.subscribe(self._update_exp_dur)
# subscribe to update X/Y res
self.stepsize.subscribe(self._update_res)
self.roi.subscribe(self._update_res)
#subscribe to binning values for camera res
self.binninghorz.subscribe(self._update_cam_res)
self.binningvert.subscribe(self._update_cam_res)
self.addMenu("Acquisition/AR Spectral...", self.start)
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, microscope, main_app):
super(ARspectral, self).__init__(microscope, main_app)
# Can only be used on a Sparc with a CCD
if not microscope:
return
main_data = self.main_app.main_data
self.ebeam = main_data.ebeam
self.ccd = main_data.ccd
self.sed = main_data.sed
self.sgrh = main_data.spectrograph
if not all((self.ebeam, self.ccd, self.sed, self.sgrh)):
logging.debug("Hardware not found, cannot use the plugin")
return
# TODO: handle SPARC systems which don't have such hardware
bigslit = model.getComponent(role="slit-in-big")
lsw = model.getComponent(role="lens-switch")
# the SEM survey stream (will be updated when showing the window)
self._survey_s = None
# Create a stream for AR spectral measurement
self._ARspectral_s = SpectralARScanStream("AR Spectrum", self.ccd,
self.sed, self.ebeam,
self.sgrh, lsw, bigslit,
main_data.opm)
# For reading the ROA and anchor ROI
self._acqui_tab = main_app.main_data.getTabByName(
"sparc_acqui").tab_data_model
# The settings to be displayed in the dialog
# Trick: we use the same VAs as the stream, so they are directly synchronised
self.centerWavelength = self._ARspectral_s.centerWavelength
#self.numberOfPixels = self._ARspectral_s.numberOfPixels
self.dwellTime = self._ARspectral_s.dwellTime
self.slitWidth = self._ARspectral_s.slitWidth
self.binninghorz = self._ARspectral_s.binninghorz
self.binningvert = self._ARspectral_s.binningvert
self.nDC = self._ARspectral_s.nDC
self.grating = model.IntEnumerated(
self.sgrh.position.value["grating"],
choices=self.sgrh.axes["grating"].choices,
setter=self._onGrating)
self.roi = self._ARspectral_s.roi
self.stepsize = self._ARspectral_s.stepsize
self.res = model.TupleVA((1, 1), unit="px")
self.cam_res = model.TupleVA((self.ccd.shape[0], self.ccd.shape[1]),
unit="px")
self.gain = self.ccd.gain
self.readoutRate = self.ccd.readoutRate
self.filename = model.StringVA("a.h5")
self.expectedDuration = model.VigilantAttribute(1,
unit="s",
readonly=True)
# Update the expected duration when values change, depends both dwell time and # of pixels
self.dwellTime.subscribe(self._update_exp_dur)
self.stepsize.subscribe(self._update_exp_dur)
self.nDC.subscribe(self._update_exp_dur)
# subscribe to update X/Y res
self.stepsize.subscribe(self._update_res)
self.roi.subscribe(self._update_res)
#subscribe to binning values for camera res
self.binninghorz.subscribe(self._update_cam_res)
self.binningvert.subscribe(self._update_cam_res)
self.addMenu("Acquisition/AR Spectral...", self.start)
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, 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)]