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 _extract_background_fired(self):
if self.pl is None: return
signal = self.SI() - self.pl.function(self.SI.energy_axis)
i = self.SI.energy2index(self.bg_span_selector.range[1])
signal[:i] = 0.
s = Spectrum({'calibration' : {'data_cube' : signal}})
s.get_calibration_from(self.SI)
interactive_ns[self.background_substracted_spectrum_name] = s
def chrono_align_and_sum(spectrum, energy_range = (None, None),
spatial_shape = None):
"""Alignment and sum of a chrono-spim SI
Parameters
----------
spectrum : Spectrum instance
Chrono-spim
energy_range : tuple of floats
energy interval in which to perform the alignment in energy units
axis : int
"""
from spectrum import Spectrum
dc = spectrum.data_cube
min_energy_size = dc.shape[0]
# i = 0
new_dc = None
# For the progress bar to work properly we must capture the output of the
# functions that are called during the alignment process
import cStringIO
import sys
capture_output = cStringIO.StringIO()
from hyperspy.misc.progressbar import progressbar
pbar = progressbar(maxval = dc.shape[2] - 1)
for i in xrange(dc.shape[2]):
pbar.update(i)
sys.stdout = capture_output
s = Spectrum({'calibration': {'data_cube' : dc[:,:,i]}})
s.get_calibration_from(spectrum)
s.find_low_loss_origin()
s.align(energy_range, progress_bar = False)
min_energy_size = min(s.data_cube.shape[0], min_energy_size)
if new_dc is None:
new_dc = s.data_cube.sum(1)
else:
new_dc = np.concatenate([new_dc[:min_energy_size],
s.data_cube.sum(1)[:min_energy_size]], 1)
sys.stdout = sys.__stdout__
pbar.finish()
spectrum.data_cube = new_dc
spectrum.get_dimensions_from_cube()
spectrum.find_low_loss_origin()
spectrum.align(energy_range)
spectrum.find_low_loss_origin()
if spatial_shape is not None:
spectrum.data_cube = spectrum.data_cube.reshape(
[spectrum.data_cube.shape[0]] + list(spatial_shape))
spectrum.data_cube = spectrum.data_cube.swapaxes(1,2)
spectrum.get_dimensions_from_cube()