def integrate_spectra(self,
xlims=None,
ylims=None,
polarisation=1,
axslice=None,
return_patch=False):
wavelength, spectra = self.get_spectra(polarisation)
if spectra.ndim > 3:
assert axslice is not None, 'slice argument required for 3d images'
if xlims is None:
x_roi = np.s_[:]
else:
x_roi = get_roi(self.x, xlims[0], xlims[1])
if ylims is None:
y_roi = np.s_[:]
else:
y_roi = get_roi(self.y, ylims[0], ylims[1])
spectra = spectra[x_roi, y_roi,
axslice, :] if spectra.ndim > 3 else spectra[
x_roi, y_roi, :]
spectrum = np.mean(spectra, axis=(0, 1))
if return_patch:
x, y = (self.x[x_roi], self.y[y_roi])
print 'averaged over a %dx%d grid' % (len(x), len(y))
patch = np.array(
[x.min(),
y.min(),
x.max() - x.min(),
y.max() - y.min()])
return wavelength, spectrum, patch
else:
return wavelength, spectrum
def wavelength2_lims(self, value):
assert len(value) == 2, 'Value must have 2 elements'
if 'wavelength2' in self.scan:
self._wavelength2_lims = value
self._wavelength2_roi = get_roi(self.scan['wavelength2'][()], value[0], value[1])
else:
print('There is no wavelength2 dataset')
def z_lims(self, value):
assert len(value) == 2, 'Value must have 2 elements'
if 'z' in self.scan:
self._z_lims = value
a = self.scan['z'][()]
a = (a - (a.min()+a.max())/2.0)
self._z_roi = get_roi(a, value[0], value[1])
else:
print('There is no z dataset')
def wavelength_lims(self, value):
assert len(value) == 2, 'Value must have 2 elements'
self._wavelength_lims = value
self._wavelength_roi = get_roi(self.scan['wavelength'][()], value[0], value[1])
def y_lims(self, value):
assert len(value) == 2, 'Value must have 2 elements'
self._y_lims = value
a = self.scan['y'][()]
a = (a - (a.min()+a.max())/2.0)
self._y_roi = get_roi(a, value[0], value[1])
