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, *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 export(filename, data):
'''
Write a CSV file:
- If the given data is AR data then just dump the phi/data array
- If the given data is spectrum data write it as series of wavelength/intensity
filename (unicode): filename of the file to create (including path).
data (model.DataArray): the data to export.
Metadata is taken directly from the DA object.
raises:
ValueError in case the spectrum does not contain wavelength metadata.
'''
if data.metadata.get(model.MD_ACQ_TYPE, None) == model.MD_AT_SPECTRUM:
dims = data.metadata.get(model.MD_DIMS, "CTZYX"[-data.ndim:])
if dims == "C" and data.ndim == 1:
spectrum_range, unit = spectrum.get_spectrum_range(data)
if unit == "m":
spectrum_range = [s * 1e9 for s in spectrum_range]
unit = "nm"
logging.debug("Exporting spectrum data to CSV")
if unit == "nm":
# turn range to nm
spectrum_tuples = [(s, d)
for s, d in zip(spectrum_range, data)]
headers = ['# wavelength (nm)', 'intensity']
else:
logging.info(
"Exporting spectrum without wavelength information")
spectrum_tuples = data.reshape(data.shape[0], 1)
headers = ['# intensity']
with open(filename, 'w') as fd:
csv_writer = csv.writer(fd)
csv_writer.writerow(headers)
csv_writer.writerows(spectrum_tuples)
elif dims == "T" and data.ndim == 1:
time_range, unit = spectrum.get_time_range(data)
if unit == "s":
time_range = [t * 1e12 for t in time_range]
unit = "ps"
logging.debug("Exporting chronogram data to CSV")
if unit == "ps":
# turn range to nm
time_tuples = [(s, d) for s, d in zip(time_range, data)]
headers = ['# Time (ps)', 'intensity']
else:
logging.info(
"Exporting chronogram without time list information")
time_tuples = data.reshape(data.shape[0], 1)
headers = ['# intensity']
with open(filename, 'w') as fd:
csv_writer = csv.writer(fd)
csv_writer.writerow(headers)
csv_writer.writerows(time_tuples)
elif data.ndim == 2 and dims == "XC":
logging.debug("Exporting spectrum-line data to CSV")
spectrum_range, unit = spectrum.get_spectrum_range(data)
if unit == "m":
spectrum_range = [s * 1e9 for s in spectrum_range]
unit = "nm"
# attach distance as first row
line_length = data.shape[1] * data.metadata[model.MD_PIXEL_SIZE][1]
distance_lin = numpy.linspace(0, line_length, data.shape[0])
distance_lin.shape = (distance_lin.shape[0], 1)
data = numpy.append(distance_lin, data, axis=1)
# Data should be in the form of (X, C+1), with the first row and column the
# distance_from_origin\wavelength
with open(filename, 'w') as fd:
csv_writer = csv.writer(fd)
# Set the 'header' in the 0,0 element
first_row = [
'distance_from_origin(m)\\wavelength(' + unit + ')'
] + spectrum_range
csv_writer.writerow(first_row)
# dump the array
csv_writer.writerows(data)
else:
raise ValueError("Unknown type of data to be exported as CSV")
elif data.metadata.get(model.MD_ACQ_TYPE,
None) == model.MD_AT_TEMPSPECTRUM:
spectrum_range, unit_c = spectrum.get_spectrum_range(data)
if unit_c == "m":
spectrum_range = [s * 1e9 for s in spectrum_range]
unit_c = "nm"
time_range, unit_t = spectrum.get_time_range(data)
if unit_t == "s":
time_range = [t * 1e12 for t in time_range]
unit_t = "ps"
headers = ["time(" + unit_t + ")\\wavelength(" + unit_c + ")"
] + spectrum_range
rows = [(t, ) + tuple(d) for t, d in zip(time_range, data)]
with open(filename, 'w') as fd:
csv_writer = csv.writer(fd)
csv_writer.writerow(headers)
csv_writer.writerows(rows)
elif data.metadata.get(model.MD_ACQ_TYPE, None) == model.MD_AT_AR:
logging.debug("Exporting AR data to CSV")
# Data should be in the form of (Y+1, X+1), with the first row and column the angles
with open(filename, 'w') as fd:
csv_writer = csv.writer(fd)
# TODO: put theta/phi angles in metadata? Read back from MD theta/phi and then add as additional line/column
# add the phi and theta values as an extra line/column in order to be displayed in the csv-file
# attach theta as first column
theta_lin = numpy.linspace(0, math.pi / 2, data.shape[0])
data = numpy.append(theta_lin.reshape(theta_lin.shape[0], 1),
data,
axis=1)
# attach phi as first row
phi_lin = numpy.linspace(0, 2 * math.pi, data.shape[1] - 1)
phi_lin = numpy.append([[0]],
phi_lin.reshape(1, phi_lin.shape[0]),
axis=1)
data = numpy.append(phi_lin, data, axis=0)
# Set the 'header' in the 0,0 element
first_row = ['theta\\phi[rad]'] + [d for d in data[0, 1:]]
csv_writer.writerow(first_row)
# dump the array
csv_writer.writerows(data[1:, :])
else:
raise ValueError(
"Unknown acquisition type of data to be exported as CSV")
def export(filename, data):
'''
Write a CSV file:
- If the given data is AR data then just dump the phi/data array
- If the given data is spectrum data write it as series of wavelength/intensity
filename (unicode): filename of the file to create (including path).
data (model.DataArray): the data to export.
Metadata is taken directly from the DA object.
raises:
ValueError in case the spectrum does not contain wavelength metadata.
'''
if data.metadata.get(model.MD_ACQ_TYPE, None) == model.MD_AT_SPECTRUM:
dims = data.metadata.get(model.MD_DIMS, "CTZYX"[-data.ndim:])
if dims == "C" and data.ndim == 1:
spectrum_range, unit = spectrum.get_spectrum_range(data)
if unit == "m":
spectrum_range = [s * 1e9 for s in spectrum_range]
unit = "nm"
logging.debug("Exporting spectrum data to CSV")
if unit == "nm":
# turn range to nm
spectrum_tuples = [(s, d) for s, d in zip(spectrum_range, data)]
headers = ['# wavelength (nm)', 'intensity']
else:
logging.info("Exporting spectrum without wavelength information")
spectrum_tuples = data.reshape(data.shape[0], 1)
headers = ['# intensity']
with open(filename, 'w') as fd:
csv_writer = csv.writer(fd)
csv_writer.writerow(headers)
csv_writer.writerows(spectrum_tuples)
elif dims == "T" and data.ndim == 1:
time_range, unit = spectrum.get_time_range(data)
if unit == "s":
time_range = [t * 1e12 for t in time_range]
unit = "ps"
logging.debug("Exporting chronogram data to CSV")
if unit == "ps":
# turn range to nm
time_tuples = [(s, d) for s, d in zip(time_range, data)]
headers = ['# Time (ps)', 'intensity']
else:
logging.info("Exporting chronogram without time list information")
time_tuples = data.reshape(data.shape[0], 1)
headers = ['# intensity']
with open(filename, 'w') as fd:
csv_writer = csv.writer(fd)
csv_writer.writerow(headers)
csv_writer.writerows(time_tuples)
elif data.ndim == 2 and dims == "XC":
logging.debug("Exporting spectrum-line data to CSV")
spectrum_range, unit = spectrum.get_spectrum_range(data)
if unit == "m":
spectrum_range = [s * 1e9 for s in spectrum_range]
unit = "nm"
# attach distance as first row
line_length = data.shape[1] * data.metadata[model.MD_PIXEL_SIZE][1]
distance_lin = numpy.linspace(0, line_length, data.shape[0])
distance_lin.shape = (distance_lin.shape[0], 1)
data = numpy.append(distance_lin, data, axis=1)
# Data should be in the form of (X, C+1), with the first row and column the
# distance_from_origin\wavelength
with open(filename, 'w') as fd:
csv_writer = csv.writer(fd)
# Set the 'header' in the 0,0 element
first_row = ['distance_from_origin(m)\\wavelength(' + unit + ')'] + spectrum_range
csv_writer.writerow(first_row)
# dump the array
csv_writer.writerows(data)
else:
raise ValueError("Unknown type of data to be exported as CSV")
elif data.metadata.get(model.MD_ACQ_TYPE, None) == model.MD_AT_TEMPSPECTRUM:
spectrum_range, unit_c = spectrum.get_spectrum_range(data)
if unit_c == "m":
spectrum_range = [s * 1e9 for s in spectrum_range]
unit_c = "nm"
time_range, unit_t = spectrum.get_time_range(data)
if unit_t == "s":
time_range = [t * 1e12 for t in time_range]
unit_t = "ps"
headers = ["time(" + unit_t + ")\\wavelength(" + unit_c + ")"] + spectrum_range
rows = [(t,) + tuple(d) for t, d in zip(time_range, data)]
with open(filename, 'w') as fd:
csv_writer = csv.writer(fd)
csv_writer.writerow(headers)
csv_writer.writerows(rows)
elif data.metadata.get(model.MD_ACQ_TYPE, None) == model.MD_AT_AR:
logging.debug("Exporting AR data to CSV")
# Data should be in the form of (Y+1, X+1), with the first row and column the angles
with open(filename, 'w') as fd:
csv_writer = csv.writer(fd)
# TODO: put theta/phi angles in metadata? Read back from MD theta/phi and then add as additional line/column
# add the phi and theta values as an extra line/column in order to be displayed in the csv-file
# attach theta as first column
theta_lin = numpy.linspace(0, math.pi / 2, data.shape[0])
data = numpy.append(theta_lin.reshape(theta_lin.shape[0], 1), data, axis=1)
# attach phi as first row
phi_lin = numpy.linspace(0, 2 * math.pi, data.shape[1]-1)
phi_lin = numpy.append([[0]], phi_lin.reshape(1, phi_lin.shape[0]), axis=1)
data = numpy.append(phi_lin, data, axis=0)
# Set the 'header' in the 0,0 element
first_row = ['theta\\phi[rad]'] + [d for d in data[0, 1:]]
csv_writer.writerow(first_row)
# dump the array
csv_writer.writerows(data[1:, :])
else:
raise ValueError("Unknown acquisition type of data to be exported as CSV")