Merge pull request #2900 from hakonanes/fix-2881

Make Bruker .bcf reader less restrictive if no element selection is found

doc/conf.py

@@ -250,7 +250,7 @@
                        'hyperspyweb': ('https://hyperspy.org/', None),
                        'matplotlib': ('https://matplotlib.org', None),
                        'numpy': ('https://docs.scipy.org/doc/numpy', None),
-                       'scipy': ('https://docs.scipy.org/doc/scipy/reference/', None),
+                       'scipy': ('https://docs.scipy.org/doc/scipy/reference', None),
                        'dask': ('https://docs.dask.org/en/latest', None),
                        'astroML': ('https://www.astroml.org/', None),
                        'sklearn': ('https://scikit-learn.org/stable', None),

doc/dev_guide/testing.rst

@@ -86,7 +86,7 @@ Or, from HyperSpy's project folder, simply:
   <https://docs.pytest.org/en/latest/customize.html>`_ for more details.
 
 The HyperSpy test suite can also be run in parallel if you have multiple CPUs
-available, using the ```pytest-xdist`` plugin <https://pypi.org/project/pytest-xdist/>`_.
+available, using the `pytest-xdist plugin <https://pypi.org/project/pytest-xdist/>`_.
 If you have the plugin installed, HyperSpy will automatically run the test suite in
 parallel on your machine.
 
@@ -106,7 +106,7 @@ that all tests in a file run in the same worker.
 
     Running tests in parallel using ``pytest-xdist`` will change the content
     and format of the output of ``pytest`` to the console. We recommend installing
-    ```pytest-sugar`` <https://pypi.org/project/pytest-sugar/>`_ to produce
+    `pytest-sugar <https://pypi.org/project/pytest-sugar/>`_ to produce
     nicer-looking output including an animated progressbar.
 
 
@@ -118,7 +118,7 @@ random or non-deterministic behaviour. They may sometimes pass or sometimes fail
 it won't always be clear why. These are usually known as "flaky" tests.
 
 One way to approach flaky tests is to rerun them, to see if the failure was a one-off.
-This can be achieved using the ```pytest-rerunfailures`` plugin <https://pypi.org/project/pytest-rerunfailures/>`_.
+This can be achieved using the `pytest-rerunfailures plugin <https://pypi.org/project/pytest-rerunfailures/>`_.
 
 .. code:: bash
 

doc/dev_guide/writing_docs.rst

@@ -57,7 +57,7 @@ Build the documentation
 To check the output of what you wrote, you can build
 the documentation by running the ``make`` command in the ``hyperspy/doc``
 directory. For example ``make html`` will build the whole documentation in
-html format. See the make command documentation for more details.
+html format. See the ``make`` command documentation for more details.
 
 To install the documentation dependencies, run either
 

doc/user_guide/eds.rst

@@ -38,9 +38,9 @@ Loading data
 All data are loaded with the :py:func:`~.io.load` function, as described in
 detail in :ref:`Loading files<loading_files>`. HyperSpy is able to import
 different formats, among them ".msa" and ".rpl" (the raw format of Oxford
-Instruments and Brucker).
+Instruments and Bruker).
 
-Here are three example for files exported by Oxford Instruments software
+Here are three examples of files exported by Oxford Instruments software
 (INCA). For a single spectrum:
 
 .. code-block:: python
@@ -518,7 +518,7 @@ EDS curve fitting
 -----------------
 
 The intensity of X-ray lines can be extracted using curve-fitting in HyperSpy.
-This example uses an EDS-SEM spectrum of a a test material (EDS-TM001) provided
+This example uses an EDS-SEM spectrum of a test material (EDS-TM001) provided
 by `BAM <http://www.webshop.bam.de>`_.
 
 First, we load the spectrum, define the chemical composition of the sample and
@@ -594,7 +594,7 @@ ranges containing no X-ray lines:
     >>> m.fit_background()
 
 The width of the X-ray lines is defined from the energy resolution (FWHM at
-Mn Ka) provided by `energy_resolution_MnKa` in `metadata`. This parameters
+Mn Ka) provided by `energy_resolution_MnKa` in `metadata`. This parameter
 can be calibrated by fitting with
 :py:meth:`~.models.edsmodel.EDSModel.calibrate_energy_axis`:
 
@@ -714,7 +714,7 @@ transformed into weight percent either with the option
 
     >>> # With s, intensities and kfactors from before
     >>> s.quantification(intensities, method='CL', factors=kfactors,
-    >>>                  composition_units='weight')
+    ...                  composition_units='weight')
     Fe (Fe_Ka): Composition = 4.96 weight percent
     Pt (Pt_La): Composition = 95.04 weight percent
 
@@ -785,19 +785,19 @@ Absorption Correction
 ^^^^^^^^^^^^^^^^^^^^^
 
 Absorption correction can be included into any of the three quantification
-methods by adding the parameter absorption_correction=True to the function.
-By default the function iterates the quantification function until of
-tolerance value of 0.5% up to a maximum number of iterations. The maximum
-number of iterations is set to 30 by default but can be increased by
-specifying max_interations= in the function call. However, typically for TEM
-experiments convergence is witness after less then 5 iterations.
+methods by adding the parameter ``absorption_correction=True`` to the function.
+By default, the function iterates the quantification function until a
+tolerance value of 0.5% up to a maximum number of iterations is reached. The
+maximum number of iterations is set to 30 by default, but can be increased by
+specifying ``max_iterations`` in the function call. However, typically for TEM
+experiments convergence is achieved after less then 5 iterations.
 
 For example:
 
 .. code-block:: python
 
         >>> s.quantification(intensities, method='cross_section',
-        >>>                  factors=factors, absorption_correction=True)
+        ...                  factors=factors, absorption_correction=True)
 
 However for the kfactor method the user must additionally provide a sample
 thickness (in nm) either as a single float value or as a numpy array with the
@@ -808,8 +808,8 @@ composition maps for each element.
 .. code-block:: python
 
         >>> s.quantification(intensities, method='CL',
-        >>>                  factors=factors, absorption_correction=True
-        >>>                  thickness = 100.)
+        ...                  factors=factors, absorption_correction=True
+        ...                  thickness=100.)
 
 At this stage absorption correction is only applicable for parallel-sided,
 thin-film samples. Absorption correction is calculated on a pixel by pixel
@@ -835,13 +835,13 @@ is available:
 .. code-block:: python
 
     >>> hs.material.mass_absorption_coefficient(
-    >>>     element='Al', energies=['C_Ka','Al_Ka'])
+    ...     element='Al', energies=['C_Ka','Al_Ka'])
     array([ 26330.38933818,    372.02616732])
 
 .. code-block:: python
 
     >>> hs.material.mass_absorption_mixture(
-    >>>     elements=['Al','Zn'], weight_percent=[50,50], energies='Al_Ka')
+    ...     elements=['Al','Zn'], weight_percent=[50,50], energies='Al_Ka')
     2587.4161643905127
 
 Electron and X-ray range
@@ -862,7 +862,7 @@ To calculate the X-ray range of Cu Ka in pure Carbon at 30kV in micron:
 .. code-block:: python
 
     >>> hs.eds.xray_range('Cu_Ka', 30., hs.material.elements.C.
-    >>>                      Physical_properties.density_gcm3)
+    ...                   Physical_properties.density_gcm3)
     7.6418811280855454
 
 To calculate the electron range in pure Copper at 30 kV in micron

doc/user_guide/eels.rst

@@ -9,7 +9,7 @@ Tools for EELS data analysis
 
 The functions described in this chapter are only available for the
 :py:class:`~._signals.eels.EELSSpectrum` class. To transform a
-:py:class:`~.signal.BaseSignal` (or subclass) into a
+:py:class:`~.signal.BaseSignal` (or subclass) into an
 :py:class:`~._signals.eels.EELSSpectrum`:
 
 .. code-block:: python
@@ -52,8 +52,6 @@ they are arranged in the order closest to 849 eV.
     >>> get_edges_near_energy(849, width=6)
     ['La_M4', 'Fe_L1']
 
-
-`
 The static method :py:meth:`~._signals.eels.EELSSpectrum.print_edges_near_energy`
 in :py:class:`~._signals.eels.EELSSpectrum` will print out a table containing
 more information about the edges.
@@ -93,7 +91,7 @@ to aid identification of edges.
    :align:   center
    :width:   500
 
-   Labels of edges can be put or remove by toggling the edge buttons.
+   Labels of edges can be put or removed by toggling the edge buttons.
 
 
 .. _eels_thickness-label:
@@ -120,13 +118,13 @@ Zero-loss peak centre and alignment
 
 The
 :py:meth:`~._signals.eels.EELSSpectrum.estimate_zero_loss_peak_centre`
-can be used to estimate the position of the zero-loss peak. The method assumes
+can be used to estimate the position of the zero-loss peak (ZLP). The method assumes
 that the ZLP is the most intense feature in the spectra. For a more general
 approach see :py:meth:`~.signal.Signal1DTools.find_peaks1D_ohaver`.
 
 The :py:meth:`~._signals.eels.EELSSpectrum.align_zero_loss_peak` can
-align the ZLP with subpixel accuracy. It is more robust and easy to use than
-:py:meth:`~.signal.Signal1DTools.align1D` for the task. Note that it is
+align the ZLP with subpixel accuracy. It is more robust and easy to use for the task
+than :py:meth:`~.signal.Signal1DTools.align1D`. Note that it is
 possible to apply the same alignment to other spectra using the `also_align`
 argument. This can be useful e.g. to align core-loss spectra acquired
 quasi-simultaneously. If there are other features in the low loss signal
@@ -161,7 +159,7 @@ of the signal and assigns the inflexion point to the first point below a
 certain tolerance.  This tolerance value can be set using the `tol` keyword.
 Currently, the method uses smoothing to reduce the impact of the noise in the
 measure. The number of points used for the smoothing window can be specified by
-the npoints keyword.
+the `npoints` keyword.
 
 
 .. _eels.kk:
@@ -220,10 +218,13 @@ Define the chemical composition of the sample
 
     >>> s.add_elements(('B', 'N'))
 
-It is worth noting that in this case the experimental parameters and the list of elements are actually automatically imported from the EELS Data Base.
+It is worth noting that in this case the experimental parameters and the list of
+elements are actually automatically imported from the EELS Data Base.
 However, with real life data, these must often be added by hand.
 
-In order to include the effect of plural scattering, the model is convolved with the loss loss spectrum in which case the low loss spectrum needs to be provided to :py:meth:`~._signals.eels.EELSSpectrum.create_model`:
+In order to include the effect of plural scattering, the model is convolved with the
+low-loss spectrum in which case the low-loss spectrum needs to be provided to
+:py:meth:`~._signals.eels.EELSSpectrum.create_model`:
 
 .. code-block:: python
 
@@ -242,7 +243,7 @@ HyperSpy has created the model and configured it automatically:
        2 |                  B_K |                  B_K |           EELSCLEdge
 
 Conveniently, all the EELS core-loss components of the added elements are added
-automatically, names after its element symbol.
+automatically, named after its element symbol:
 
 .. code-block:: python
 
@@ -253,14 +254,14 @@ automatically, names after its element symbol.
 
 By default the fine structure features are disabled (although
 the default value can be configured (see :ref:`configuring-hyperspy-label`).
-We must enable them to accurately fit this spectrum.
+We must enable them to accurately fit this spectrum:
 
 .. code-block:: python
 
     >>> m.enable_fine_structure()
 
 
-We use :py:meth:`~.models.eelsmodel.EELSModel.smart_fit` instead of standard
+We use :py:meth:`~.models.eelsmodel.EELSModel.smart_fit` instead of the standard
 fit method because :py:meth:`~.models.eelsmodel.EELSModel.smart_fit` is
 optimized to fit EELS core-loss spectra
 
@@ -279,11 +280,11 @@ image
 .. NOTE::
 
     `m.smart_fit()` and `m.multifit(kind='smart')` are methods specific to the
-    EELS model. The fitting procedure acts in iterative manner along the
+    EELS model. The fitting procedure acts in an iterative manner along the
     energy-loss-axis. First it fits only the background up to the first edge.
     It continues by deactivating all edges except the first one, then performs
     the fit. Then it only activates the the first two, fits, and repeats this
-    until all edges are fitted simultanously.
+    until all edges are fitted simultaneously.
 
     Other, non-EELSCLEdge components, are never deactivated, and fitted on every
     iteration.
@@ -328,7 +329,7 @@ There are several methods that are only available in
   convolving with a zero-loss peak.
 
 The following methods permit to easily enable/disable background and ionisation
-edges components:
+edge components:
 
 * :py:meth:`~.models.eelsmodel.EELSModel.enable_edges`
 * :py:meth:`~.models.eelsmodel.EELSModel.enable_background`
@@ -353,10 +354,9 @@ When fitting edges with fine structure enabled it is often desirable that the
 fine structure region of nearby ionization edges does not overlap. HyperSpy
 provides a method,
 :py:meth:`~.models.eelsmodel.EELSModel.resolve_fine_structure`, to
-automatically adjust the fine structure to prevent fine structure to avoid
-overlapping. This method is executed automatically when e.g. components are
-added or removed from the model, but sometimes is necessary to call it
-manually.
+automatically adjust the fine structure to avoid overlap. This method is executed
+automatically when e.g. components are added or removed from the model, but
+sometimes is necessary to call it manually.
 
 Sometimes it is desirable to disable the automatic adjustment of the fine
 structure width. It is possible to suspend this feature by calling

doc/user_guide/model.rst

@@ -1569,7 +1569,7 @@ Strategies
 
 During operation SAMFire uses a list of strategies to determine how to select
 the next pixel and estimate its starting parameters. Only one strategy is used
-at a time. Next strategy is chosen when no new pixels are can be fitted with
+at a time. Next strategy is chosen when no new pixels can be fitted with
 the current strategy. Once either the strategy list is exhausted or the full
 dataset fitted, the algorithm terminates.
 
@@ -1645,7 +1645,7 @@ it's recommended specify it explicitly via the ``ipyparallel=False`` argument,
 to use the fall-back option of `multiprocessing`.
 
 By default a new SAMFire object already has two (and currently only) strategies
-added to its strategist list:
+added to its ``strategies`` list:
 
 .. code-block:: python
 

hyperspy/io_plugins/bruker.py

@@ -748,8 +748,11 @@ def _set_elements(self, root):
             for j in elements.findall(
                     "./ClassInstance[@Type='TRTSpectrumRegion']"):
                 tmp_d = dictionarize(j)
-                self.elements[tmp_d['XmlClassName']] = {'line': tmp_d['Line'],
-                                                        'energy': tmp_d['Energy']}
+                # In case no information on the specific selected X-ray line is
+                # available, assume it is a 'Ka' line, reflecting the fact that element
+                # tables in Bruker Esprit (1.9 and 2.1) store a single K line for Li-Al
+                self.elements[tmp_d['XmlClassName']] = {'line': tmp_d.get('Line', 'Ka'),
+                                                        'energy': tmp_d.get('Energy')}
         except AttributeError:
             _logger.info('no element selection present in the spectra..')
 

hyperspy/models/edsmodel.py

@@ -602,11 +602,10 @@ def calibrate_energy_axis(self,
         calibrate: 'resolution' or 'scale' or 'offset'
             If 'resolution', fits the width of Gaussians place at all x-ray
             lines. The width is given by a model of the detector resolution,
-            obtained by extrapolating the `energy_resolution_MnKa` in `metadata`
-            `metadata`.
+            obtained by extrapolating the `energy_resolution_MnKa` in `metadata`.
             This method will update the value of `energy_resolution_MnKa`.
-            If 'scale', calibrate the scale of the energy axis
-            If 'offset', calibrate the offset of the energy axis
+            If 'scale', calibrate the scale of the energy axis.
+            If 'offset', calibrate the offset of the energy axis.
         xray_lines: list of str or 'all_alpha'
             The Xray lines. If 'all_alpha', fit all using all alpha lines
         **kwargs : extra key word arguments
@@ -641,9 +640,9 @@ def free_sub_xray_lines_weight(self, xray_lines='all', bound=0.01):
 
         Parameters
         ----------
-        xray_lines: list of str or 'all'
+        xray_lines : list of str or 'all'
             The Xray lines. If 'all', fit all lines
-        bounds: float
+        bound : float
             Bound the height of the peak to a fraction of
             its height
         """

upcoming_changes/2881.bugfix.rst

@@ -0,0 +1,2 @@
+In case the Bruker defined XML element node at SpectrumRegion contains no information on the
+specific selected X-ray line (if there is only single line available), suppose it is 'Ka' line.

upcoming_changes/README.rst

@@ -1,4 +1,4 @@
-This directory contains "newsfragments" which are short files that contain a small **ReST**-formatted
+This directory contains "news fragments" which are short files that contain a small **ReST**-formatted
 text that will be added to the next ``CHANGELOG``.
 
 The ``CHANGELOG`` will be read by **users**, so this description should be aimed to hyperspy users
@@ -12,27 +12,25 @@ Each file should be named like ``<ISSUE>.<TYPE>.rst``, where
 * ``doc``: documentation improvement, like rewording an entire session or adding missing docs.
 * ``deprecation``: feature deprecation.
 * ``enhancements``: improvement of existing functionality, usually without requiring user intervention.
-* ``api``: a change which may break existing script, such as feature removal or behavior change.
+* ``api``: a change which may break an existing script, such as feature removal or behavior change.
 * ``maintenance``: a change related to the test suite, packaging, etc.
 
-So for example: ``123.new.rst``, ``456.bugfix.rst``.
+So for example ``1412.new.rst`` or ``2773.bugfix.rst``.
 
-If your PR fixes an issue, use that number here. If there is no issue,
+If your PR fixes an issue, use the number of the issue here. If there is no issue,
 then after you submit the PR and get the PR number you can add a
 changelog using that instead.
 
 If you are not sure what issue type to use, don't hesitate to ask in your PR.
 
 ``towncrier`` preserves multiple paragraphs and formatting (code blocks, lists, and so on), but for entries
 other than ``new`` it is usually better to stick to a single paragraph to keep it concise. For ``new``,
-it is recommended to add hyperlink to the user guide.
+it is recommended to add a hyperlink to the user guide.
 
-
-To previous a draft of the changelog, run from the command line:
+To make a draft of the changelog, run from the command line:
 
    .. code-block:: bash
 
        $ towncrier build --draft
 
-
 See https://github.com/twisted/towncrier for more details.