fix many glossary terms (#331)

* Update .github/workflows/flakeheaven.yml

* added packages

* fix many glossary terms

* added blacken-docs

* testing blacken-docs

* testing blacken-docs

* testing blacken-docs

* testing action suggester

* testing action suggester

* testing action suggester

* remove additional file commits

* added run step for blacken-docs

* testing action-suggester with flakeheaven

* updated glossary

* updated glossary references

* added :chem: role

* changed chem_role setup

* merge from master branch

* removed m/z & peak from glossary

* removed additional lower level glossary terms

* merged master branch

* added missinge linenos

* changed indentation

* merge with master

* chagend tsv separator to t

* use other suggestion action

* Update code-blocks-linting.yaml

---------

Co-authored-by: matteopilz <[email protected]>

.github/workflows/code-blocks-linting.yaml

@@ -32,7 +32,11 @@ jobs:
            exit 1
         fi
 
-    - name: Run action-suggester
+    - name: Run action-suggester (only works on lines that were changes in this PR)
       uses: reviewdog/action-suggester@v1
       with:
         tool_name: blacken-docs
+
+    - name: Display diff and fail if there was anything changed by blacken-docs
+      run: |
+        git diff --diff-algorithm histogram --exit-code

.gitignore

@@ -4,3 +4,4 @@
 docs/source/_autosummary/
 docs/source/_build/
 docs/build/
+docs/source/_autosummary

docs/source/_ext/chemrole.py

@@ -0,0 +1,15 @@
+from docutils.nodes import math
+
+
+def chem_role(name, rawtext, text, lineno, inliner, options={}, content=[]):
+    latex = rf'\ce{{{text}}}'
+    node = math(rawtext, latex, **options)
+    return [node], []
+
+
+def setup(app):
+    """Install the plugin.
+    :param app: Sphinx application context.
+    """
+    app.add_role('chem', chem_role)
+    return {'parallel_read_safe': True}

docs/source/algorithms.rst

@@ -34,9 +34,10 @@ pattern are :py:class:`~.GaussFilter`, :py:class:`~.SavitzkyGolayFilter` as well
 :py:class:`~.ParentPeakMower`, :py:class:`~.Scaler`, :py:class:`~.SpectraMerger`, :py:class:`~.SqrtMower`,
 :py:class:`~.ThresholdMower`, :py:class:`~.WindowMower`.
 
-Using the same example file as before, we can execute a GaussFilter on our test data as follows: 
+Using the same example file as before, we can execute a :py:class:`~.GaussFilter` on our test data as follows:
 
 .. code-block:: python
+    :linenos:
 
     from pyopenms import *
     from urllib.request import urlretrieve

docs/source/build_from_source.rst

@@ -1,4 +1,4 @@
-Build from source
+Build from Source
 ==================
 
 To install pyOpenMS from :index:`source`, you will first have to compile OpenMS
@@ -12,7 +12,7 @@ following software packages
 On Microsoft Windows: you need the 64 bit C++ compiler from Visual Studio 2015
 to compile the newest pyOpenMS for Python 3.5, 3.6 or 3.7. This is important,
 else you get a clib that is different than the one used for building the Python
-executable, and pyOpenMS will crash on import. The OpenMS wiki has `detailed information 
+executable, and pyOpenMS will crash on import. The OpenMS wiki has `detailed information
 <https://github.com/OpenMS/OpenMS/wiki/Build-pyOpenMS-on-Windows>`_ 
 on building pyOpenMS on Windows.
 
@@ -43,7 +43,7 @@ that the correct Python executable is used. Compiling pyOpenMS can use a lot of
 memory and take some time, however you can reduce the memory consumption by
 breaking up the compilation into multiple units and compiling in parallel, for
 example ``-DPY_NUM_THREADS=2 -DPY_NUM_MODULES=4`` will build 4 modules with 2
-threads. You can then configure pyOpenMS: 
+threads. You can then configure pyOpenMS:
 
 .. code-block:: bash
 
@@ -59,7 +59,7 @@ Afterwards, test that all went well by running the tests:
 
 Which should execute all the tests and return with all tests passing.
 
-Further questions 
+Further Questions
 *****************
 
 In case the above instructions did not work, please refer to the `Wiki Page

docs/source/centroiding.rst

@@ -2,15 +2,15 @@ Centroiding
 ===========
 
 MS instruments typically allow storing spectra in profile mode (several data points per m/z peak)
-or in the more codensed centroid mode (one data point per m/z peak). The process of converting
-a profile spectrum into a centroided one is called peak centroiding or peak picking.
+or in the more condensed centroid mode (one data point per m/z peak). The process of converting
+a profile mass spectrum into a centroided one is called peak centroiding or peak picking.
 
-Note: The term peak picking is ambiguous as it is also used for feature detection (i.e. 3D "peak" finding).
+Note: The term peak picking is ambiguous as it is also used for features detection (i.e. 3D peak finding).
 
 First, we load some profile data:
 
 .. code-block:: python
-
+    :linenos:
     from urllib.request import urlretrieve
     from pyopenms import *
     import matplotlib.pyplot as plt
@@ -24,21 +24,21 @@ First, we load some profile data:
 Let's zoom in on an isotopic pattern in profile mode and plot it.
 
 .. code-block:: python
-
+    :linenos:
     plt.xlim(771.8, 774)  # zoom into isotopic pattern
     plt.plot(
         profile_spectra[0].get_peaks()[0], profile_spectra[0].get_peaks()[1]
     )  # plot the first spectrum
 
 .. image:: img/profile_data.png
 
-Because of the limited resolution of MS instruments m/z measurements are not of unlimited precision. 
-Consequently, peak shapes spreads in the m/z dimension and resemble a gaussian distribution.
-Using the PeakPickerHiRes algorithm, we can convert data from profile to centroided mode. Usually, not much information is lost
+Because of the limited resolution of MS instruments m/z measurements are not of unlimited precision.
+Consequently, peak  shapes spreads in the m/z dimension and resemble a gaussian distribution.
+Using the :py:class:`~.PeakPickerHiRes` algorithm, we can convert data from profile to centroided mode. Usually, not much information is lost
 by storing only centroided data. Thus, many algorithms and tools assume that centroided data is provided.
 
 .. code-block:: python
-
+    :linenos:
     centroided_spectra = MSExperiment()
 
     # input, output, chec_spectrum_type (if set, checks spectrum type and throws an exception if a centroided spectrum is passed)
@@ -52,6 +52,6 @@ by storing only centroided data. Thus, many algorithms and tools assume that cen
     )  # plot as vertical lines
 .. image:: img/centroided_data.png
 
-After centroding, a single m/z value for every isotopic peak is retained. By plotting the centroided data as stem plot
+After centroiding, a single m/z value for every isotopic peak is retained. By plotting the centroided data as stem plot
 we discover that (in addition to the isotopic peaks) some low intensity peaks (intensity at approx. 4k) were present in the profile data.
 

docs/source/deisotoping.rst → docs/source/charge_isotope_deconvolution.rst

@@ -3,27 +3,28 @@ Charge and Isotope Deconvolution
 
 A single mass spectrum contains measurements of one or more analytes and the
 m/z values recorded for these analytes. Most analytes produce multiple signals
-in the mass spectrometer, due to the natural abundance of carbon 13 (naturally
-occurring at ca. 1% frequency) and the large amount of carbon atoms in most
+in the mass spectrometer, due to the natural abundance of carbon :math:`13` (naturally
+occurring at ca. :math:`1%` frequency) and the large amount of carbon atoms in most
 organic molecules, most analytes produce a so-called isotopic pattern with a
-monoisotopic peak (all carbon are C12) and a first isotopic peak (exactly one
-carbon atom is a C13), a second isotopic peak (exactly two atoms are C13) etc.
+monoisotopic peak (all carbon are :chem:`^{12}C`) and a first isotopic peak (exactly one
+carbon atom is a :chem:`^{13}C`), a second isotopic peak (exactly two atoms are :chem:`^{13}C`) etc.
 Note that also other elements can contribute to the isotope pattern, see the 
-`Chemistry section <chemistry.html>`_ for further details.
+`chemistry section <chemistry.html>`_ for further details.
 
 In addition, each analyte may appear in more than one charge state and adduct
-state, a singly charge analyte ``[M+H]+`` may be accompanied by a doubly
-charged analyte ``[M+2H]++`` or a sodium adduct ``[M+Na]+``. In the case of a
+state, a singly charge analyte :chem:`[M +H]+` may be accompanied by a doubly
+charged analyte :chem:`[M +2H]++` or a sodium adduct :chem:`[M +Na]+`. In the case of a
 multiply charged peptide, the isotopic traces are spaced by ``PROTON_MASS /
-charge_state`` which is often close to 0.5 m/z for doubly charged analytes,
-0.33 m/z for triply charged analytes etc.  Note: tryptic peptides often appear
+charge_state`` which is often close to :math:`0.5\ m/z` for doubly charged analytes,
+:math:`0.33\ m/z` for triply charged analytes etc. Note: tryptic peptides often appear
 at least doubly charged, while small molecules often carry a single charge but
 can have adducts other than hydrogen.
 
-Single peak example
-*******************
+Single Peak Example
+*********************************
 
 .. code-block:: python
+    :linenos:
 
     from pyopenms import *
 
@@ -50,10 +51,11 @@ Note that the algorithm presented here as some heuristics built into it, such
 as assuming that the isotopic peaks will decrease after the first isotopic
 peak. This heuristic can be tuned by changing the parameter
 ``use_decreasing_model`` and ``start_intensity_check``. In this case, the
-second isotopic peak is the highest in intensity and the
+second isotopic peak  is the highest in intensity and the
 ``start_intensity_check`` parameter needs to be set to 3. 
 
 .. code-block:: python
+    :linenos:
 
     charge = 4
     seq = AASequence.fromString("DFPIANGERDFPIANGERDFPIANGERDFPIANGER")
@@ -93,7 +95,7 @@ second isotopic peak is the highest in intensity and the
         print(p.getMZ(), p.getIntensity())
 
 
-Full spectral de-isotoping
+Full Spectral De-Isotoping
 **************************
 
 In the following code segment, we will use a sample measurement of BSA (Bovine
@@ -102,6 +104,7 @@ mass spectrum, which means grouping peaks of the same isotopic pattern charge
 state:
 
 .. code-block:: python
+    :linenos:
 
     from urllib.request import urlretrieve
 
@@ -155,35 +158,35 @@ which produces the following output
   974.4572680576728 6200571.5
   974.4589691256419 3215808.75
 
-As we can see, the algorithm has reduced 140 peaks to 41 deisotoped peaks. It
-also has identified a molecule at 974.45 m/z as the most intense peak in the
-data (basepeak).
+As we can see, the algorithm has reduced :math:`140` peaks to :math:`41` deisotoped peaks. It
+also has identified a molecule at :math:`974.45\ m/z` as the most intense peak in the
+data (base peak).
 
 Visualization
 *************
 
 The reason we see two peaks very close together becomes apparent
-once we look at the data in TOPPView which indicates that the 974.4572680576728
-peak is derived from a 2+ peak at m/z 487.73 and the peak at 974.4589691256419
-is derived from a 3+ peak at m/z 325.49: the algorithm has identified a single
+once we look at the data in :term:`TOPPView` which indicates that the :math:`974.4572680576728`
+peak is derived from a :chem:`2+` peak at m/z :math:`487.73` and the peak at :math:`974.4589691256419`
+is derived from a :chem:`3+` peak at m/z :math:`325.49`: the algorithm has identified a single
 analyte in two charge states and deconvoluted the peaks to their nominal mass
-of a ``[M+H]+`` ion, which produces two peaks very close together (2+ and 3+
+of a :chem:`[M +H]+` ion, which produces two peaks very close together (:chem:`2+` and :chem:`3+`
 peak):
 
 .. image:: img/deisotoped_zoom.png
 
-Looking at the full spectrum and comparing it to the original spectrum, we can see the
-original (centroided) spectrum on the top and the deisotoped spectrum on the
-bottom in blue. Note how hovering over a peak in the deisotoped spectrum
+Looking at the full mass spectrum and comparing it to the original mass spectrum, we can see the
+original (centroided) mass spectrum on the top and the deisotoped mass spectrum on the
+bottom in blue. Note how hovering over a peak in the deisotoped mass spectrum
 indicates the charge state:
 
 .. image:: img/deisotoped.png
 
-In the next section, we will look at 2-dimensional deisotoping where instead of
-a single spectrum, multiple spectra from a :term:`LC-MS` experiments are analyzed
+In the next section (`Feature Detection <feature_detection.html>`_), we will look at 2-dimensional deisotoping where instead of
+a single mass spectrum, multiple mass spectra from a :term:`LC-MS` experiment are analyzed
 together. There algorithms analyze the full 2-dimensional (m/z and RT) signal
 and are generally more powerful than the 1-dimensional algorithm discussed
 here. However, not all data is 2 dimensional and the algorithm discussed here
 has many application in practice (e.g. single mass spectra, fragment ion
-spectra in DDA etc.).
+mass spectra in DDA etc.).