def _spectrum_from_component(self, layer, component, wcs, mask=None):
data = SpectralCube(component.data, wcs)
if mask is not None:
data = data.with_mask(mask)
if self._data_operation.currentIndex() == 1:
spec_data = data.mean((1, 2))
elif self._data_operation.currentIndex() == 2:
spec_data = data.median((1, 2))
else:
spec_data = data.sum((1, 2))
spec_data = Spectrum1DRef(spec_data.data,
unit=spec_data.unit,
dispersion=data.spectral_axis.data,
dispersion_unit=data.spectral_axis.unit,
wcs=data.wcs,
name=layer.label)
# Store the relation between the component and the specviz data. If
# the data exists, first remove the component from specviz and then
# re-add it.
if layer in self._specviz_data_cache:
old_spec_data = self._specviz_data_cache[layer]
dispatch.on_remove_data.emit(old_spec_data)
self._specviz_data_cache[layer] = spec_data
dispatch.on_add_to_window.emit(data=spec_data,
style={'color': layer.style.rgba[:3]})
def _spectrum_from_component(self,
layer,
component,
wcs,
mask=None,
cid=None):
data = SpectralCube(component.data, wcs)
if mask is not None:
data = data.with_mask(mask)
# Update the associated data attribute in the plugin
self._spec_ops.spectral_data = data
self._spec_ops.component_id = cid
if self._data_operation.currentIndex() == 1:
spec_data = data.mean((1, 2))
elif self._data_operation.currentIndex() == 2:
spec_data = data.median((1, 2))
else:
spec_data = data.sum((1, 2))
spec_data = Spectrum1DRef(spec_data.data,
unit=spec_data.unit,
dispersion=data.spectral_axis.data,
dispersion_unit=data.spectral_axis.unit,
wcs=data.wcs,
name=layer.label)
spec_layer = Spectrum1DRefLayer.from_parent(spec_data)
# Store the relation between the component and the specviz data. If
# the data exists, first remove the component from specviz and then
# re-add it.
old_spec_layer = self._specviz_data_cache.get(layer)
self._specviz_data_cache[layer] = spec_layer
if old_spec_layer is None:
dispatch.on_add_to_window.emit(layer=spec_layer,
style={
'color': layer.style.rgba[:3],
'line_width': 3
},
vertical_line=True)
else:
dispatch.replace_layer.emit(old_layer=old_spec_layer,
new_layer=spec_layer,
style={
'color': layer.style.rgba[:3],
'line_width': 3
})
header=cubeA.header,
)
outpath = 'RatioCube_DendrogramObjects{0}.fits'.format(sm)
rcube.write(hpath(outpath), overwrite=True)
max_temcube = tcube.max(axis=0)
max_temcube.hdu.writeto(hpath(
'TemperatureCube_DendrogramObjects{0}_Piecewise_max.fits'.format(sm)),
clobber=True)
max_rcube = rcube.max(axis=0)
max_rcube.hdu.writeto(hpath(
'RatioCube_DendrogramObjects{0}_Piecewise_max.fits'.format(sm)),
clobber=True)
mean_temcube = tcube.mean(axis=0)
mean_temcube.hdu.writeto(hpath(
'TemperatureCube_DendrogramObjects{0}_Piecewise_mean.fits'.format(sm)),
clobber=True)
mean_rcube = rcube.mean(axis=0)
mean_rcube.hdu.writeto(hpath(
'RatioCube_DendrogramObjects{0}_Piecewise_mean.fits'.format(sm)),
clobber=True)
hdu_template = mean_rcube.hdu
tcube = tcube.filled_data[:].value
weight_cube = cube303sm if 'smooth' in sm else cube303
weights = weight_cube.filled_data[:].value
weights[weights < 0] = 0
mean_temcube = np.nansum(tcube * weights, axis=0) / np.nansum(weights,
tcube.write(hpath(outpath), overwrite=True)
rcube = SpectralCube(data=rcubedata, wcs=cubeA.wcs,
mask=cubeA.mask, meta={'unit':'K'},
header=cubeA.header,
)
outpath = 'RatioCube_DendrogramObjects{0}.fits'.format(sm)
rcube.write(hpath(outpath), overwrite=True)
max_temcube = tcube.max(axis=0)
max_temcube.hdu.writeto(hpath('TemperatureCube_DendrogramObjects{0}_Piecewise_max.fits'.format(sm)), clobber=True)
max_rcube = rcube.max(axis=0)
max_rcube.hdu.writeto(hpath('RatioCube_DendrogramObjects{0}_Piecewise_max.fits'.format(sm)), clobber=True)
mean_temcube = tcube.mean(axis=0)
mean_temcube.hdu.writeto(hpath('TemperatureCube_DendrogramObjects{0}_Piecewise_mean.fits'.format(sm)), clobber=True)
mean_rcube = rcube.mean(axis=0)
mean_rcube.hdu.writeto(hpath('RatioCube_DendrogramObjects{0}_Piecewise_mean.fits'.format(sm)), clobber=True)
hdu_template = mean_rcube.hdu
tcube = tcube.filled_data[:].value
weight_cube = cube303sm if 'smooth' in sm else cube303
weights = weight_cube.filled_data[:].value
weights[weights < 0] = 0
mean_temcube = np.nansum(tcube*weights, axis=0) / np.nansum(weights, axis=0)
hdu_template.data = mean_temcube
hdu_template.writeto(hpath('TemperatureCube_DendrogramObjects{0}_Piecewise_weightedmean.fits'.format(sm)), clobber=True)
mean_rcube = np.nansum(rcube*weights,axis=0) / np.nansum(weights, axis=0)
hdu_template.data = mean_rcube
