Remove unnecessary code and check if the parameters are of the correc…

…t type and length

hyperspy/_signals/eds_tem.py

@@ -25,7 +25,7 @@
 from scipy import constants
 import pint
 
-from hyperspy.signal import BaseSetMetadataItems
+from hyperspy.signal import BaseSetMetadataItems, BaseSignal
 from hyperspy import utils
 from hyperspy._signals.eds import (EDSSpectrum, LazyEDSSpectrum)
 from hyperspy.defaults_parser import preferences
@@ -399,13 +399,22 @@ def quantification(self,
         --------
         vacuum_mask
         """
+        if (not isinstance(intensities, (list, tuple)) or
+                not isinstance(intensities[0], BaseSignal)):
+            raise ValueError(
+                "The parameter `intensities` must be a list of signals."
+                )
+        elif len(intensities) <= 1:
+            raise ValueError("Several X-ray line intensities are required.")
+
         if isinstance(navigation_mask, float):
             if self.axes_manager.navigation_dimension > 0:
                 navigation_mask = self.vacuum_mask(navigation_mask, closing)
             else:
                 navigation_mask = None
 
-        xray_lines = [intensity.metadata.Sample.xray_lines[0] for intensity in intensities]
+        xray_lines = [intensity.metadata.Sample.xray_lines[0]
+                      for intensity in intensities]
         it = 0
         if absorption_correction:
             if show_progressbar is None:  # pragma: no cover
@@ -559,7 +568,7 @@ def quantification(self,
             utils.plot.plot_signals(composition, **kwargs)
 
         if absorption_correction:
-            _logger.info(f'Convergence reach after {it} interations.')
+            _logger.info(f'Convergence reached after {it} interations.')
 
         if method == 'zeta':
             mass_thickness.metadata.General.title = 'Mass thickness'

hyperspy/misc/eds/utils.py

@@ -422,55 +422,36 @@ def quantification_cliff_lorimer(intensities,
     # Value used as an threshold to prevent using zeros as denominator
     min_intensity = 0.1
     dim = intensities.shape
-    if len(dim) > 1:
-        dim2 = reduce(lambda x, y: x * y, dim[1:])
-        intens = intensities.reshape(dim[0], dim2)
-        intens = intens.astype('float')
-
-        if absorption_correction is None:
-            # default to ones
-            absorption_correction = np.ones_like(intens, dtype=float)
-        else:
-            absorption_correction = absorption_correction.reshape(dim[0], dim2)
-
-        for i in range(dim2):
-            index = np.where(intens[:, i] > min_intensity)[0]
-            if len(index) > 1:
-                ref_index, ref_index2 = index[:2]
-                intens[:, i] = _quantification_cliff_lorimer(
-                    intens[:, i], kfactors, absorption_correction[:, i],
-                    ref_index, ref_index2)
-            else:
-                intens[:, i] = np.zeros_like(intens[:, i])
-                if len(index) == 1:
-                    intens[index[0], i] = 1.
-
-        intens = intens.reshape(dim)
-        if mask is not None:
-            from hyperspy.signals import BaseSignal
-            if isinstance(mask, BaseSignal):
-                mask = mask.data
-            for i in range(dim[0]):
-                intens[i][(mask==True)] = 0
-        return intens
+    dim2 = reduce(lambda x, y: x * y, dim[1:])
+    intens = intensities.reshape(dim[0], dim2).astype(float)
+
+    if absorption_correction is None:
+        # default to ones
+        absorption_correction = np.ones_like(intens, dtype=float)
     else:
-        index = np.where(intensities > min_intensity)[0]
-        if absorption_correction is None:
-            # default to ones
-            absorption_correction = np.ones_like(intensities, dtype=float)
+        absorption_correction = absorption_correction.reshape(dim[0], dim2)
+
+    for i in range(dim2):
+        index = np.where(intens[:, i] > min_intensity)[0]
         if len(index) > 1:
             ref_index, ref_index2 = index[:2]
-            intens = _quantification_cliff_lorimer(
-                intensities,
-                kfactors,
-                absorption_correction,
-                ref_index,
-                ref_index2)
+            intens[:, i] = _quantification_cliff_lorimer(
+                intens[:, i], kfactors, absorption_correction[:, i],
+                ref_index, ref_index2)
         else:
-            intens = np.zeros_like(intensities)
+            intens[:, i] = np.zeros_like(intens[:, i])
             if len(index) == 1:
-                intens[index[0]] = 1.
-        return intens
+                intens[index[0], i] = 1.
+
+    intens = intens.reshape(dim)
+    if mask is not None:
+        from hyperspy.signals import BaseSignal
+        if isinstance(mask, BaseSignal):
+            mask = mask.data
+        for i in range(dim[0]):
+            intens[i][(mask==True)] = 0
+
+    return intens
 
 quantification_cliff_lorimer.__doc__ %= (_ABSORPTION_CORRECTION_DOCSTRING)
 
@@ -635,11 +616,11 @@ def quantification_cross_section(intensities,
 
     if absorption_correction is None:
         # default to ones
-        absorption_correction = np.ones_like(intensities, dtype='float')
+        absorption_correction = np.ones_like(intensities, dtype=float)
 
     shp = len(intensities.shape) - 1
     slices = (slice(None),) + (None,) * shp
-    x_sections = np.array(cross_sections, dtype='float')[slices]
+    x_sections = np.array(cross_sections, dtype=float)[slices]
     number_of_atoms = intensities / (x_sections * dose * 1e-10) * absorption_correction
     total_atoms = np.cumsum(number_of_atoms, axis=0)[-1]
     composition = number_of_atoms / total_atoms

hyperspy/tests/signals/test_eds_tem.py

@@ -202,6 +202,19 @@ def test_metadata(self):
             TEM_md.probe_area, 1.2)
         np.testing.assert_approx_equal(TEM_md.Detector.EDS.real_time, 3.1)
 
+    def test_quant_one_intensity_error(self):
+        s = self.signal
+        method = 'CL'
+        kfactors = [1, 2.0009344042484134]
+        intensities = s.get_lines_intensity()[:1]
+        assert len(intensities) == 1    
+        with pytest.raises(ValueError):
+            _ = s.quantification(intensities, method, kfactors)
+
+        intensities = s.get_lines_intensity()[0]
+        with pytest.raises(ValueError):
+            _ = s.quantification(intensities, method, kfactors)
+
     def test_quant_lorimer(self):
         s = self.signal
         method = 'CL'
@@ -281,22 +294,7 @@ def test_quant_lorimer_ac(self):
                                    atol=1e-3)
         np.testing.assert_allclose(res3[0][0].data, np.ones((2, 2)) * 31.816908,
                                    atol=1e-3)
-        np.testing.assert_allclose(res[0].data, res4[0][0].data, atol=1e-5)
-
-    def test_quant_lorimer_ac_single_spectrum(self):
-        s = self.signal.inav[0]
-        method = 'CL'
-        kfactors = [1, 2.0009344042484134]
-        composition_units = 'weight'
-        intensities = s.get_lines_intensity()
-        res = s.quantification(intensities, method, kfactors,
-                               composition_units)
-        np.testing.assert_allclose(res[0].data, 22.70779, atol=1e-3)
-        res2 = s.quantification(intensities, method, kfactors,
-                                composition_units,
-                                absorption_correction=True,
-                                thickness=1.)
-        np.testing.assert_allclose(res2[0].data, 22.743013, atol=1e-3)        
+        np.testing.assert_allclose(res[0].data, res4[0][0].data, atol=1e-5)     
 
     def test_quant_zeta(self):
         s = self.signal