def to_spectrum(self):
from hyperspy.signals.spectrum import Spectrum
dic = self._get_signal_dict()
dim = len(self.data.shape)
dic['mapped_parameters']['record_by'] = 'spectrum'
dic['data'] = np.rollaxis(dic['data'], 0, dim)
dic['axes'] = utils_varia.rollelem(dic['axes'],0, dim)
i = 0
for axis in dic['axes']:
axis['index_in_array'] = i
i += 1
sp = Spectrum(dic)
sp.axes_manager._set_axes_index_in_array_from_position()
if hasattr(self, 'learning_results'):
sp.learning_results = copy.deepcopy(self.learning_results)
sp.learning_results._transpose_results()
sp.learning_results.original_shape = self.data.shape
return sp
def to_spectrum(self, signal_axis=0):
"""Image to spectrum
Parameters
----------
signal_axis : integer
Selected the signal axis.
Examples
--------
>>> img = signals.Image({'data' : np.ones((3,4,5,6))})
>>> img
<Image, title: , dimensions: (3L, 4L, 5L, 6L)>
>>> img.to_spectrum()
<Spectrum, title: , dimensions: (4L, 5L, 6L, 3L)>
>>> img.to_spectrum(1)
<Spectrum, title: , dimensions: (3L, 5L, 6L, 4L)>
"""
from hyperspy.signals.spectrum import Spectrum
dic = self._get_signal_dict()
dim = len(self.data.shape)
dic['mapped_parameters']['record_by'] = 'spectrum'
dic['data'] = np.rollaxis(dic['data'], signal_axis, dim)
dic['axes'] = utils_varia.rollelem(dic['axes'], signal_axis, dim)
i = 0
for axis in dic['axes']:
axis['index_in_array'] = i
i += 1
sp = Spectrum(dic)
sp.axes_manager._set_axes_index_in_array_from_position()
if hasattr(self, 'learning_results'):
if signal_axis != 0 and self.learning_results.loadings is not None:
print("The learning results won't be transfered correctly")
else :
sp.learning_results = copy.deepcopy(self.learning_results)
sp.learning_results._transpose_results()
sp.learning_results.original_shape = self.data.shape
sp.tmp_parameters = self.tmp_parameters.deepcopy()
return sp