Vacuum mask eels

hyperspy/_signals/eds_tem.py

@@ -291,16 +291,16 @@ def quantification(self,
                        intensities,
                        method,
                        factors,
-                       composition_units = 'atomic',
-                       absorption_correction = False,
-                       take_off_angle = 'auto',
-                       thickness = 'auto',
-                       convergence_criterion = 0.5,
-                       navigation_mask = 1.0,
-                       closing = True,
-                       plot_result = False,
-                       probe_area = 'auto',
-                       max_iterations = 30,
+                       composition_units='atomic',
+                       absorption_correction=False,
+                       take_off_angle='auto',
+                       thickness='auto',
+                       convergence_criterion=0.5,
+                       navigation_mask=1.0,
+                       closing=True,
+                       plot_result=False,
+                       probe_area='auto',
+                       max_iterations=30,
                        **kwargs):
         """
         Absorption corrected quantification using Cliff-Lorimer, the zeta-factor
@@ -342,7 +342,8 @@ def quantification(self,
             The navigation locations marked as True are not used in the
             quantification. If float is given the vacuum_mask method is used to
             generate a mask with the float value as threhsold.
-            Else provides a signal with the navigation shape.
+            Else provides a signal with the navigation shape. Only for the
+            'Cliff-Lorimer' method.
         closing: bool
             If true, applied a morphologic closing to the mask obtained by
             vacuum_mask.
@@ -389,9 +390,7 @@ def quantification(self,
         vacuum_mask
         """
         if isinstance(navigation_mask, float):
-            navigation_mask = self.vacuum_mask(navigation_mask, closing).data
-        elif navigation_mask is not None:
-            navigation_mask = navigation_mask.data
+            navigation_mask = self.vacuum_mask(navigation_mask, closing)
 
         xray_lines = [intensity.metadata.Sample.xray_lines[0] for intensity in intensities]
         it = 0
@@ -421,32 +420,30 @@ def quantification(self,
 
         abs_corr_factor = None # initial
 
+        quant_kwargs = {"intensities" : int_stack.data,
+                        "absorption_correction" : abs_corr_factor}
+
         if method == 'CL':
             quantification_method = utils_eds.quantification_cliff_lorimer
-            kwargs = {"intensities" : int_stack.data,
-                    "kfactors" : factors,
-                    "absorption_correction" : abs_corr_factor}
+            quant_kwargs.update({"kfactors" : factors,
+                                 "mask": navigation_mask})
 
         elif method == 'zeta':
             quantification_method = utils_eds.quantification_zeta_factor
-            kwargs = {"intensities" : int_stack.data,
-                    "zfactors" : factors,
-                    "dose" : self._get_dose(method),
-                    "absorption_correction" : abs_corr_factor}
+            quant_kwargs.update({"zfactors" : factors,
+                                 "dose" : self._get_dose(method)})
 
         elif method =='cross_section':
             quantification_method = utils_eds.quantification_cross_section
-            kwargs = {"intensities" : int_stack.data,
-                    "cross_sections" : factors,
-                    "dose" : self._get_dose(method, **kwargs),
-                    "absorption_correction" : abs_corr_factor}
+            quant_kwargs.update({"cross_sections" : factors,
+                                 "dose" : self._get_dose(method)})
 
         else:
             raise ValueError('Please specify method for quantification, '
-                             'as \'CL\', \'zeta\' or \'cross_section\'.')
+                             'as "CL", "zeta" or "cross_section".')
 
         while True:
-            results = quantification_method(**kwargs)
+            results = quantification_method(**quant_kwargs)
 
             if method == 'CL':
                 composition.data = results * 100.
@@ -477,13 +474,13 @@ def quantification(self,
                                                        number_of_atoms.split(),
                                                        toa,
                                                        probe_area)
-                kwargs["absorption_correction"] = abs_corr_factor
+                quant_kwargs["absorption_correction"] = abs_corr_factor
             else:
                 if absorption_correction:
                     abs_corr_factor = utils_eds.get_abs_corr_zeta(composition.split(),
                                                        mass_thickness,
                                                        toa)
-                    kwargs["absorption_correction"] = abs_corr_factor
+                    quant_kwargs["absorption_correction"] = abs_corr_factor
 
             res_max = np.max((composition - comp_old).data)
             comp_old.data = composition.data
@@ -557,9 +554,6 @@ def quantification(self,
                 return composition, mass_thickness
             else:
                 return composition
-        else:
-            raise ValueError('Please specify method for quantification, as \
-            ''CL\', \'zeta\' or \'cross_section\'')
 
 
     def vacuum_mask(self, threshold=1.0, closing=True, opening=False):
@@ -576,6 +570,11 @@ def vacuum_mask(self, threshold=1.0, closing=True, opening=False):
         opnening: bool
             If true, applied a morphologic opening to the mask
 
+        Returns
+        -------
+        mask: signal
+            The mask of the region
+
         Examples
         --------
         >>> # Simulate a spectrum image with vacuum region
@@ -586,11 +585,6 @@ def vacuum_mask(self, threshold=1.0, closing=True, opening=False):
         >>> si = hs.stack([s]*3 + [s_vac])
         >>> si.vacuum_mask().data
         array([False, False, False,  True], dtype=bool)
-
-        Return
-        ------
-        mask: signal
-            The mask of the region
         """
         from scipy.ndimage.morphology import binary_dilation, binary_erosion
         mask = (self.max(-1) <= threshold)

hyperspy/_signals/eels.py

@@ -24,8 +24,9 @@
 import traits.api as t
 from scipy import constants
 from prettytable import PrettyTable
+import pint
 
-from hyperspy.signal import BaseSetMetadataItems
+from hyperspy.signal import BaseSetMetadataItems, BaseSignal
 from hyperspy._signals.signal1d import (Signal1D, LazySignal1D)
 from hyperspy.signal_tools import EdgesRange
 from hyperspy.misc.elements import elements as elements_db
@@ -48,6 +49,7 @@
 
 
 _logger = logging.getLogger(__name__)
+_ureg = pint.UnitRegistry()
 
 
 @add_gui_method(toolkey="hyperspy.microscope_parameters_EELS")
@@ -272,10 +274,10 @@ def estimate_zero_loss_peak_centre(self, mask=None):
 
         Parameters
         ----------
-        mask : Signal1D of bool data type.
+        mask : Signal1D of bool data type or bool array
             It must have signal_dimension = 0 and navigation_shape equal to the
-            current signal. Where mask is True the shift is not computed
-            and set to nan.
+            navigation shape of the current signal. Where mask is True the
+            shift is not computed and set to nan.
 
         Returns
         -------
@@ -296,12 +298,14 @@ def estimate_zero_loss_peak_centre(self, mask=None):
         """
         self._check_signal_dimension_equals_one()
         self._check_navigation_mask(mask)
+        if isinstance(mask, BaseSignal):
+            mask = mask.data
         zlpc = self.valuemax(-1)
         if mask is not None:
             if zlpc._lazy:
-                zlpc.data = da.where(mask.data, np.nan, zlpc.data)
+                zlpc.data = da.where(mask, np.nan, zlpc.data)
             else:
-                zlpc.data[mask.data] = np.nan
+                zlpc.data[mask] = np.nan
         zlpc.set_signal_type("")
         title = self.metadata.General.title
         zlpc.metadata.General.title = "ZLP(%s)" % title
@@ -341,10 +345,10 @@ def align_zero_loss_peak(
         subpixel : bool
             If True, perform the alignment with subpixel accuracy
             using cross-correlation.
-        mask : Signal1D of bool data type.
-            It must have signal_dimension = 0 and navigation_shape equal to the
-            current signal. Where mask is True the shift is not computed
-            and set to nan.
+        mask : Signal1D of bool data type or bool array.
+            It must have signal_dimension = 0 and navigation_shape equal to
+            the shape of the current signal. Where mask is True the shift is
+            not computed and set to nan.
         signal_range : tuple of integers, tuple of floats. Optional
             Will only search for the ZLP within the signal_range. If given
             in integers, the range will be in index values. If given floats,
@@ -1003,21 +1007,21 @@ def fourier_ratio_deconvolution(self, ll,
                 'after_fourier_ratio_deconvolution')
         return cl
 
-    def richardson_lucy_deconvolution(self, psf, iterations=15, mask=None,
+    def richardson_lucy_deconvolution(self, psf, iterations=15,
                                       show_progressbar=None,
                                       parallel=None, max_workers=None):
         """1D Richardson-Lucy Poissonian deconvolution of
         the spectrum by the given kernel.
 
         Parameters
         ----------
-        iterations: int
-            Number of iterations of the deconvolution. Note that
-            increasing the value will increase the noise amplification.
-        psf: EELSSpectrum
+        psf : EELSSpectrum
             It must have the same signal dimension as the current
             spectrum and a spatial dimension of 0 or the same as the
             current spectrum.
+        iterations : int
+            Number of iterations of the deconvolution. Note that
+            increasing the value will increase the noise amplification.
         %s
         %s
         %s
@@ -1034,8 +1038,6 @@ def richardson_lucy_deconvolution(self, psf, iterations=15, mask=None,
             show_progressbar = preferences.General.show_progressbar
         self._check_signal_dimension_equals_one()
         psf_size = psf.axes_manager.signal_axes[0].size
-        kernel = psf()
-        imax = kernel.argmax()
         maxval = self.axes_manager.navigation_size
         show_progressbar = show_progressbar and (maxval > 0)
 
@@ -1049,6 +1051,7 @@ def deconv_function(signal, kernel=None,
                 result *= np.convolve(kernel[::-1], signal /
                                       first)[mimax:mimax + psf_size]
             return result
+
         ds = self.map(deconv_function, kernel=psf, iterations=iterations,
                       psf_size=psf_size, show_progressbar=show_progressbar,
                       parallel=parallel, max_workers=max_workers,
@@ -1108,29 +1111,30 @@ def set_microscope_parameters(self,
                 "Acquisition_instrument.TEM.Detector.EELS.collection_angle",
                 collection_angle)
     set_microscope_parameters.__doc__ = \
-        """
-        Set the microscope parameters that are necessary to calculate
-        the GOS.
-
-        If not all of them are defined, in interactive mode
-        raises an UI item to fill the values
-
-        beam_energy: float
-            The energy of the electron beam in keV
-        convengence_angle : float
-            The microscope convergence semi-angle in mrad.
-        collection_angle : float
-            The collection semi-angle in mrad.
-        {}
-        {}
-        """.format(TOOLKIT_DT, DISPLAY_DT)
+"""
+Set the microscope parameters that are necessary to calculate
+the GOS. If not all of them are defined, in interactive mode
+raises an UI item to fill the values.
+
+Parameters
+----------
+
+beam_energy:  float
+    The energy of the electron beam in keV.
+convengence_angle : float
+    The microscope convergence semi-angle in mrad.
+collection_angle : float
+    The collection semi-angle in mrad.
+{}
+{}
+""".format(TOOLKIT_DT, DISPLAY_DT)
 
     def power_law_extrapolation(self,
                                 window_size=20,
                                 extrapolation_size=1024,
                                 add_noise=False,
                                 fix_neg_r=False):
-        """Extrapolate the spectrum to the right using a powerlaw
+        """Extrapolate the spectrum to the right using a powerlaw.
 
 
         Parameters
@@ -1609,7 +1613,7 @@ def plot_edges_label(self, edges, vertical_line_marker=None,
             vertical_line_marker, text_marker = slp.get_markers(edges)
             # the object is needed to connect replot method when axes_manager
             # indices changed
-            er = EdgesRange(self, active=list(edges.keys()))
+            _ = EdgesRange(self, active=list(edges.keys()))
         if len(vertical_line_marker) != len(text_marker) or \
             len(edges) != len(vertical_line_marker):
             raise ValueError('The size of edges, vertical_line_marker and '
@@ -1788,6 +1792,45 @@ def rebin(self, new_shape=None, scale=None, crop=True, out=None):
         return m
     rebin.__doc__ = hyperspy.signal.BaseSignal.rebin.__doc__
 
+    def vacuum_mask(self, threshold=10.0, start_energy=None,
+                    closing=True, opening=False):
+        """
+        Generate mask of the vacuum region
+
+        Parameters
+        ----------
+        threshold: float
+            For a given navigation coordinate, mean value in the energy axis
+            below which the pixel is considered as vacuum.
+        start_energy: float, None
+            Minimum energy included in the calculation of the mean intensity.
+            If None, consider only the last quarter of the spectrum to
+            calculate the mask.
+        closing: bool
+            If True, a morphological closing is applied to the mask.
+        opening: bool
+            If True, a morphological opening is applied to the mask.
+
+        Returns
+        -------
+        mask: signal
+            The mask of the region.
+        """
+        signal_axis = self.axes_manager.signal_axes[0]
+        if start_energy is None:
+            start_energy = 0.75 * signal_axis.high_value
+
+        mask = (self.isig[start_energy:].mean(-1) <= threshold)
+
+        from scipy.ndimage.morphology import binary_dilation, binary_erosion
+        if closing:
+            mask.data = binary_dilation(mask.data, border_value=0)
+            mask.data = binary_erosion(mask.data, border_value=1)
+        if opening:
+            mask.data = binary_erosion(mask.data, border_value=1)
+            mask.data = binary_dilation(mask.data, border_value=0)
+        return mask
+
 
 class EELSSpectrum(EELSSpectrum_mixin, Signal1D):
 

hyperspy/_signals/signal1d.py

@@ -562,10 +562,7 @@ def _check_navigation_mask(self, mask):
             elif mask.axes_manager.signal_dimension not in (0, 1):
                 raise ValueError("mask must be a BaseSignal "
                                  "with signal_dimension equal to 1")
-            elif (mask.axes_manager.navigation_dimension !=
-                  self.axes_manager.navigation_dimension):
-                raise ValueError("mask must be a BaseSignal with the same "
-                                 "navigation_dimension as the current signal.")
+            super()._check_navigation_mask(mask)
 
     def estimate_shift1D(
         self,

hyperspy/learn/mva.py

@@ -232,6 +232,8 @@ def decomposition(
         * :py:meth:`~._signals.lazy.LazySignal.decomposition` for lazy signals
 
         """
+        from hyperspy.signal import BaseSignal
+
         # Check data is suitable for decomposition
         if self.data.dtype.char not in np.typecodes["AllFloat"]:
             raise TypeError(
@@ -358,7 +360,9 @@ def decomposition(
         self._unfolded4decomposition = self.unfold()
         try:
             _logger.info("Performing decomposition analysis")
-
+            if isinstance(navigation_mask, BaseSignal):
+                self._check_navigation_mask(mask=navigation_mask)
+                navigation_mask = navigation_mask.data
             if hasattr(navigation_mask, "ravel"):
                 navigation_mask = navigation_mask.ravel()