def export(self, export_type, raw=False):
"""
Returns the data to be exported with respect to the settings and options.
:param export_type (string): spatial, AR, spectrum or spectrum-line
:param raw (boolean): raw data format if True
:param interpolate_data (boolean): apply interpolation on data if True
returns DataArray or list of DataArray: the data to be exported, either
an RGB image or raw data.
raises:
LookupError: if no data found to export
"""
fview = self._data_model.focussedView.value
vp = self.get_viewport_by_view(fview)
streams = fview.getStreams()
if export_type == 'AR':
# TODO: set MD_ACQ_TYPE in stream code
for i, _ in enumerate(streams):
for r in streams[i].raw:
r.metadata[model.MD_ACQ_TYPE] = model.MD_AT_AR
exported_data = ar_to_export_data(streams, raw)
elif export_type == 'spectrum':
spectrum = vp.stream.get_pixel_spectrum()
spectrum_range, unit = vp.stream.get_spectrum_range()
spectrum.metadata[model.MD_ACQ_TYPE] = model.MD_AT_SPECTRUM
exported_data = spectrum_to_export_data(spectrum, raw, unit, spectrum_range)
elif export_type == 'spectrum-line':
spectrum_md = vp.stream.get_pixel_spectrum().metadata
spectrum = vp.stream.get_line_spectrum()
# copy only non existed values (we mainly care about MD_WL_*)
spectrum.metadata.update({k: v for k, v in spectrum_md.iteritems() if k not in spectrum.metadata})
spectrum_range, unit = vp.stream.get_spectrum_range()
spectrum.metadata[model.MD_ACQ_TYPE] = model.MD_AT_SPECTRUM
exported_data = line_to_export_data(spectrum, raw, unit, spectrum_range)
else:
export_type = 'spatial'
view_px = tuple(vp.canvas.ClientSize)
view_mpp = fview.mpp.value
view_hfw = (view_mpp * view_px[0], view_mpp * view_px[1])
view_pos = fview.view_pos.value
draw_merge_ratio = fview.stream_tree.kwargs.get("merge", 0.5)
interpolate_data = fview.interpolate_content.value
exported_data = images_to_export_data(streams,
view_hfw, view_pos,
draw_merge_ratio, raw,
interpolate_data=interpolate_data,
logo=self._main_frame.legend_logo)
return exported_data
def test_ar_export(self):
filename = "test-ar.csv"
# Create AR data
md = {
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake ccd",
model.MD_DESCRIPTION: "AR",
model.MD_ACQ_TYPE: model.MD_AT_AR,
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_SENSOR_PIXEL_SIZE: (13e-6, 13e-6), # m/px
model.MD_PIXEL_SIZE: (2e-5, 2e-5), # m/px
model.MD_POS: (1.2e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_AR_POLE: (253.1, 65.1),
model.MD_LENS_MAG: 0.4, # ratio
}
md0 = dict(md)
data0 = model.DataArray(1500 + numpy.zeros((512, 512), dtype=numpy.uint16), md0)
md1 = dict(md)
md1[model.MD_POS] = (1.5e-3, -30e-3)
md1[model.MD_BASELINE] = 300 # AR background should take this into account
data1 = model.DataArray(3345 + numpy.zeros((512, 512), dtype=numpy.uint16), md1)
# Create AR stream
ars = stream.StaticARStream("test", [data0, data1])
ars.point.value = md1[model.MD_POS]
# Convert to exportable RGB image
exdata = img.ar_to_export_data([ars], raw=True)
# shape = raw data + theta/phi axes values
self.assertGreater(exdata.shape[0], 50)
self.assertGreater(exdata.shape[1], 50)
# Save into a CSV file
exporter = dataio.get_converter("CSV")
exporter.export(filename, exdata)
st = os.stat(filename) # this test also that the file is created
self.assertGreater(st.st_size, 100)
# clean up
try:
os.remove(filename)
except Exception:
pass
def test_ar_export(self):
filename = "test-ar.csv"
# Create AR data
md = {
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake ccd",
model.MD_DESCRIPTION: "AR",
model.MD_ACQ_TYPE: model.MD_AT_AR,
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_SENSOR_PIXEL_SIZE: (13e-6, 13e-6), # m/px
model.MD_PIXEL_SIZE: (2e-5, 2e-5), # m/px
model.MD_POS: (1.2e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_AR_POLE: (253.1, 65.1),
model.MD_LENS_MAG: 0.4, # ratio
}
md0 = dict(md)
data0 = model.DataArray(1500 + numpy.zeros((512, 512), dtype=numpy.uint16), md0)
md1 = dict(md)
md1[model.MD_POS] = (1.5e-3, -30e-3)
md1[model.MD_BASELINE] = 300 # AR background should take this into account
data1 = model.DataArray(3345 + numpy.zeros((512, 512), dtype=numpy.uint16), md1)
# Create AR stream
ars = stream.StaticARStream("test", [data0, data1])
ars.point.value = md1[model.MD_POS]
# Convert to exportable RGB image
exdata = img.ar_to_export_data([ars], raw=True)
# shape = raw data + theta/phi axes values
self.assertGreater(exdata.shape[0], 50)
self.assertGreater(exdata.shape[1], 50)
# Save into a CSV file
exporter = dataio.get_converter("CSV")
exporter.export(filename, exdata)
st = os.stat(filename) # this test also that the file is created
self.assertGreater(st.st_size, 100)
# clean up
try:
os.remove(filename)
except Exception:
pass
def export(self, export_type, raw=False):
"""
Returns the data to be exported with respect to the settings and options.
:param export_type (string): spatial, AR, spectrum, spectrum-temporal,
spectrum-time, or spectrum-line
:param raw (boolean): raw data format if True
:param interpolate_data (boolean): apply interpolation on data if True
returns DataArray or list of DataArray: the data to be exported, either
an RGB image or raw data.
raises:
LookupError: if no data found to export
"""
fview = self._data_model.focussedView.value
vp = self.get_viewport_by_view(fview)
streams = fview.stream_tree.getProjections(
) # Stream tree to get the DataProjection
if export_type == 'AR':
exported_data = ar_to_export_data(streams, raw)
elif export_type == 'spectrum':
exported_data = spectrum_to_export_data(streams[0], raw, vp)
elif export_type == 'spectrum-line':
exported_data = line_to_export_data(streams[0], raw)
elif export_type == 'spectrum-temporal':
exported_data = temporal_spectrum_to_export_data(streams[0], raw)
elif export_type == 'spectrum-time':
exported_data = chronogram_to_export_data(streams[0], raw, vp)
else:
view_px = tuple(vp.canvas.ClientSize)
view_mpp = fview.mpp.value
view_hfw = (view_mpp * view_px[0], view_mpp * view_px[1])
view_pos = fview.view_pos.value
draw_merge_ratio = fview.stream_tree.kwargs.get("merge", 0.5)
interpolate_data = fview.interpolate_content.value
exported_data = images_to_export_data(
streams,
view_hfw,
view_pos,
draw_merge_ratio,
raw,
vp.canvas,
interpolate_data=interpolate_data,
logo=self._main_frame.legend_logo)
return exported_data
def export_ar_to_csv(fn, background=None):
"""
fn (str): full path to the AR data file
background (DataArray or None): background data to subtract
"""
das = dataio.open_acquisition(fn)
if not das: # No such file or file doesn't contain data
return
streams = dataio.data_to_static_streams(das)
# Remove the extension of the filename, to extend the name with .csv
fn_base = dataio.splitext(fn)[0]
ar_streams = [s for s in streams if isinstance(s, ARStream)]
for s in ar_streams:
try:
s.background.value = background
except Exception as ex:
logging.error("Failed to use background data: %s", ex)
ar_proj = stream.ARRawProjection(s)
# Export every position separately
for p in s.point.choices:
if p == (None,
None): # Special "non-selected point" => not interesting
continue
s.point.value = p
# Project to "raw" = Theta vs phi array
exdata = img.ar_to_export_data([ar_proj], raw=True)
# Pick a good name
fn_csv = fn_base
if len(ar_streams) > 1: # Add the name of the stream
fn_csv += "-" + s.name.value
if len(s.point.choices) > 2:
# More than one point in the stream => add position (in µm)
fn_csv += f"-{p[0] * 1e6}-{p[1] * 1e6}"
fn_csv += ".csv"
# Save into a CSV file
logging.info("Exporting point %s to %s", p, fn_csv)
csv.export(fn_csv, exdata)
def export(self, export_type, raw=False):
"""
Returns the data to be exported with respect to the settings and options.
:param export_type (string): spatial, AR, spectrum, spectrum-temporal,
spectrum-time, or spectrum-line
:param raw (boolean): raw data format if True
:param interpolate_data (boolean): apply interpolation on data if True
returns DataArray or list of DataArray: the data to be exported, either
an RGB image or raw data.
raises:
LookupError: if no data found to export
"""
fview = self._data_model.focussedView.value
vp = self.get_viewport_by_view(fview)
streams = fview.stream_tree.getProjections() # Stream tree to get the DataProjection
if export_type == 'AR':
exported_data = ar_to_export_data(streams, raw)
elif export_type == 'spectrum':
exported_data = spectrum_to_export_data(streams[0], raw, vp)
elif export_type == 'spectrum-line':
exported_data = line_to_export_data(streams[0], raw)
elif export_type == 'spectrum-temporal':
exported_data = temporal_spectrum_to_export_data(streams[0], raw)
elif export_type == 'spectrum-time':
exported_data = chronogram_to_export_data(streams[0], raw, vp)
else:
view_px = tuple(vp.canvas.ClientSize)
view_mpp = fview.mpp.value
view_hfw = (view_mpp * view_px[0], view_mpp * view_px[1])
view_pos = fview.view_pos.value
draw_merge_ratio = fview.stream_tree.kwargs.get("merge", 0.5)
interpolate_data = fview.interpolate_content.value
exported_data = images_to_export_data(streams,
view_hfw, view_pos,
draw_merge_ratio, raw,
interpolate_data=interpolate_data,
logo=self._main_frame.legend_logo)
return exported_data
def test_big_ar_export(self):
# Create AR data
md = {
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake ccd",
model.MD_DESCRIPTION: "AR",
model.MD_ACQ_TYPE: model.MD_AT_AR,
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_SENSOR_PIXEL_SIZE: (13e-6, 13e-6), # m/px
model.MD_PIXEL_SIZE: (4e-5, 4e-5), # m/px
model.MD_POS: (1.2e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_AR_POLE: (500, 500),
}
md0 = dict(md)
data0 = model.DataArray(1500 + numpy.zeros((1080, 1024), dtype=numpy.uint16), md0)
md1 = dict(md)
md1[model.MD_POS] = (1.5e-3, -30e-3)
md1[model.MD_BASELINE] = 300 # AR background should take this into account
data1 = model.DataArray(500 + numpy.zeros((1080, 1024), dtype=numpy.uint16), md1)
# Create AR stream
ars = stream.StaticARStream("test", [data0])
ars.point.value = md0[model.MD_POS]
# Wait for the projection to be computed
tend = time.time() + 90
while ars.image.value is None:
self.assertLess(time.time(), tend, "Timeout during AR computation")
time.sleep(0.1)
# Convert to exportable RGB image
exdata = img.ar_to_export_data([ars], raw=False)
# shape = RGBA
self.assertGreater(exdata.shape[0], 200)
self.assertGreater(exdata.shape[1], 200)
self.assertEqual(exdata.shape[2], 4)
# The top-left corner should be white
numpy.testing.assert_equal(exdata[0, 0], [255, 255, 255, 255])
# There should be some non-white data
self.assertTrue(numpy.any(exdata != 255))
# Save into a PNG file
exporter = dataio.get_converter("PNG")
exporter.export(self.FILENAME_PR, exdata)
st = os.stat(self.FILENAME_PR) # this test also that the file is created
self.assertGreater(st.st_size, 1000)
# Convert to equirectangular (RAW) image
exdata = img.ar_to_export_data([ars], raw=True)
# shape = raw data + theta/phi axes values
self.assertGreater(exdata.shape[0], 50)
self.assertGreater(exdata.shape[1], 50)
# Save into a CSV file
exporter = dataio.get_converter("CSV")
exporter.export(self.FILENAME_RAW, exdata)
st = os.stat(self.FILENAME_RAW) # this test also that the file is created
self.assertGreater(st.st_size, 100)
# Create AR stream with background image
ars.background.value = data1
# Convert to equirectangular (RAW) image
exdata = img.ar_to_export_data([ars], raw=True)
# shape = raw data + theta/phi axes values
self.assertGreater(exdata.shape[0], 50)
self.assertGreater(exdata.shape[1], 50)
def test_big_ar_export(self):
# Create AR data
md = {
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake ccd",
model.MD_DESCRIPTION: "AR",
model.MD_ACQ_TYPE: model.MD_AT_AR,
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_SENSOR_PIXEL_SIZE: (13e-6, 13e-6), # m/px
model.MD_PIXEL_SIZE: (4e-5, 4e-5), # m/px
model.MD_POS: (1.2e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_AR_POLE: (500, 500),
}
md0 = dict(md)
data0 = model.DataArray(
1500 + numpy.zeros((1080, 1024), dtype=numpy.uint16), md0)
md1 = dict(md)
md1[model.MD_POS] = (1.5e-3, -30e-3)
md1[model.
MD_BASELINE] = 300 # AR background should take this into account
data1 = model.DataArray(
500 + numpy.zeros((1080, 1024), dtype=numpy.uint16), md1)
# Create AR stream
ars = stream.StaticARStream("test", [data0])
ars.point.value = md0[model.MD_POS]
# Wait for the projection to be computed
tend = time.time() + 90
while ars.image.value is None:
self.assertLess(time.time(), tend, "Timeout during AR computation")
time.sleep(0.1)
# Convert to exportable RGB image
exdata = img.ar_to_export_data([ars], raw=False)
# shape = RGBA
self.assertGreater(exdata.shape[0], 200)
self.assertGreater(exdata.shape[1], 200)
self.assertEqual(exdata.shape[2], 4)
# The top-left corner should be white
numpy.testing.assert_equal(exdata[0, 0], [255, 255, 255, 255])
# There should be some non-white data
self.assertTrue(numpy.any(exdata != 255))
# Save into a PNG file
exporter = dataio.get_converter("PNG")
exporter.export(self.FILENAME_PR, exdata)
st = os.stat(
self.FILENAME_PR) # this test also that the file is created
self.assertGreater(st.st_size, 1000)
# Convert to equirectangular (RAW) image
exdata = img.ar_to_export_data([ars], raw=True)
# shape = raw data + theta/phi axes values
self.assertGreater(exdata.shape[0], 50)
self.assertGreater(exdata.shape[1], 50)
# Save into a CSV file
exporter = dataio.get_converter("CSV")
exporter.export(self.FILENAME_RAW, exdata)
st = os.stat(
self.FILENAME_RAW) # this test also that the file is created
self.assertGreater(st.st_size, 100)
# Create AR stream with background image
ars.background.value = data1
# Convert to equirectangular (RAW) image
exdata = img.ar_to_export_data([ars], raw=True)
# shape = raw data + theta/phi axes values
self.assertGreater(exdata.shape[0], 50)
self.assertGreater(exdata.shape[1], 50)
