def save_principal_components(self, n, spectrum_prefix = 'pc',
image_prefix = 'im', spectrum_format = 'msa', image_format = 'tif',
on_peaks=False):
"""Save the `n` first principal components and score maps
in the specified format
Parameters
----------
n : int
Number of principal components to save_als_ica_results
image_prefix : string
Prefix for the image file names
spectrum_prefix : string
Prefix for the spectrum file names
spectrum_format : string
image_format : string
"""
from spectrum import Spectrum
target=self._get_target(on_peaks)
im_list = self.plot_principal_components_maps(n, plot = False,
on_peaks=on_peaks)
s = Spectrum({'calibration' : {'data_cube' : target.pc[:,0]}})
s.get_calibration_from(self)
for i in xrange(n):
s.data_cube = target.pc[:,i]
s.get_dimensions_from_cube()
s.save('%s-%i.%s' % (spectrum_prefix, i, spectrum_format))
im_list[i].save('%s-%i.%s' % (image_prefix, i, image_format))
def save_principal_components(self,
n,
spectrum_prefix='pc',
image_prefix='im',
spectrum_format='msa',
image_format='tif',
on_peaks=False):
"""Save the `n` first principal components and score maps
in the specified format
Parameters
----------
n : int
Number of principal components to save_als_ica_results
image_prefix : string
Prefix for the image file names
spectrum_prefix : string
Prefix for the spectrum file names
spectrum_format : string
image_format : string
"""
from spectrum import Spectrum
target = self._get_target(on_peaks)
im_list = self.plot_principal_components_maps(n,
plot=False,
on_peaks=on_peaks)
s = Spectrum({'calibration': {'data_cube': target.pc[:, 0]}})
s.get_calibration_from(self)
for i in xrange(n):
s.data_cube = target.pc[:, i]
s.get_dimensions_from_cube()
s.save('%s-%i.%s' % (spectrum_prefix, i, spectrum_format))
im_list[i].save('%s-%i.%s' % (image_prefix, i, image_format))
def save_independent_components(self,
elements=None,
spectrum_format='msa',
image_format='tif',
recmatrix=None,
ic=None,
on_peaks=False):
"""Saves the result of the ICA in image and spectrum format.
Note that to save the image, the NaNs in the map will be converted
to zeros.
Parameters
----------
elements : None or tuple of strings
a list of names (normally an element) to be assigned to IC. If not
the will be name ic-0, ic-1 ...
image_format : string
spectrum_format : string
recmatrix : None or numpy array
externally supplied recmatrix
ic : None or numpy array
externally supplied IC
"""
from hyperspy.signals.spectrum import Spectrum
target = self._get_target(on_peaks)
pl = self.plot_independent_components_maps(plot=False,
recmatrix=recmatrix,
ic=ic,
no_nans=True,
on_peaks=on_peaks)
if ic is None:
ic = target.ic
if self.data.shape[2] > 1:
maps = True
else:
maps = False
for i in xrange(ic.shape[1]):
axes = (self.axes_manager._slicing_axes[0].get_axis_dictionary(), )
axes[0]['index_in_array'] = 0
spectrum = Spectrum({'data': ic[:, i], 'axes': axes})
spectrum.data_cube = ic[:, i].reshape((-1, 1, 1))
if elements is None:
spectrum.save('ic-%s.%s' % (i, spectrum_format))
if maps is True:
pl[i].save('map_ic-%s.%s' % (i, image_format))
else:
pl[i].save('profile_ic-%s.%s' % (i, spectrum_format))
else:
element = elements[i]
spectrum.save('ic-%s.%s' % (element, spectrum_format))
if maps:
pl[i].save('map_ic-%s.%s' % (element, image_format))
else:
pl[i].save('profile_ic-%s.%s' % (element, spectrum_format))
def save_independent_components(self, elements=None,
spectrum_format='msa',
image_format='tif',
recmatrix=None, ic=None,
on_peaks=False):
"""Saves the result of the ICA in image and spectrum format.
Note that to save the image, the NaNs in the map will be converted
to zeros.
Parameters
----------
elements : None or tuple of strings
a list of names (normally an element) to be assigned to IC. If not
the will be name ic-0, ic-1 ...
image_format : string
spectrum_format : string
recmatrix : None or numpy array
externally supplied recmatrix
ic : None or numpy array
externally supplied IC
"""
from hyperspy.signals.spectrum import Spectrum
target=self._get_target(on_peaks)
pl = self.plot_independent_components_maps(plot=False,
recmatrix=recmatrix,
ic=ic,
no_nans=True,
on_peaks=on_peaks)
if ic is None:
ic = target.ic
if self.data.shape[2] > 1:
maps = True
else:
maps = False
for i in xrange(ic.shape[1]):
axes = (self.axes_manager._slicing_axes[0].get_axis_dictionary(),)
axes[0]['index_in_array'] = 0
spectrum = Spectrum({'data' : ic[:,i], 'axes' : axes})
spectrum.data_cube = ic[:,i].reshape((-1,1,1))
if elements is None:
spectrum.save('ic-%s.%s' % (i, spectrum_format))
if maps is True:
pl[i].save('map_ic-%s.%s' % (i, image_format))
else:
pl[i].save('profile_ic-%s.%s' % (i, spectrum_format))
else:
element = elements[i]
spectrum.save('ic-%s.%s' % (element, spectrum_format))
if maps:
pl[i].save('map_ic-%s.%s' % (element, image_format))
else:
pl[i].save('profile_ic-%s.%s' % (element, spectrum_format))
