Merge branch 'master' of https://github.com/OpenMS/pyopenms-docs into fixs

Author: cbielow

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

@@ -16,7 +16,7 @@ jobs:
 
     - name: Get changed files
       id: changed-files
-      uses: tj-actions/changed-files@v41
+      uses: tj-actions/changed-files@v46
       with:
         dir_names_max_depth: 0
         files: |

.github/workflows/test-pr.yaml

@@ -19,7 +19,7 @@ jobs:
 
     - name: Get changed files
       id: changed-files
-      uses: tj-actions/changed-files@v45
+      uses: tj-actions/changed-files@v46
       with:
           dir_names_max_depth: 0
           files: |

docs/source/_static/OpenMS.svg

@@ -70,7 +70,7 @@ p.rubric+dl.py.function {
 /* Parameter names should look a bit different from types */
 .sig-param > span.n:nth-child(1) > .pre {
   font-weight: 500;
-  color: #16324F;
+  color: #3f87d3;
 }
 
 /* In desktop mode, when the top navbar is filled, make the padding to
@@ -118,11 +118,6 @@ p.rubric+dl.py.function {
   100% {transform: rotate(-35deg) translate(0px, 0px);}
 }
 
-/* Enlarge logo */
-.navbar-brand img {
-    scale: 2.0
-}
-
 /* In the right tocs:
  The edit pencil icon is a bit bigger than the show source file icon.
  Therefore pad it. */

docs/source/_static/custom.css

@@ -150,6 +150,11 @@
             # The type of image to be used (see below for details)
             "type": "fontawesome",
         },
+        {
+            "name": "Discord",
+            "url": "https://discord.com/invite/4TAGhqJ7s5",
+            "icon": "fa-brands fa-discord",
+        },
         {
             "name": "PyPI",
             "url": "https://pypi.org/project/pyopenms",

docs/source/conf.py

@@ -124,6 +124,7 @@ Let's look at a very heavy peptide, whose isotopic distribution is dominated by
         use_decreasing_model,
         start_intensity_check,
         False,
+        True
     )
     for p in s:
         print("Mono peaks:", p.getMZ(), p.getIntensity())
@@ -156,6 +157,7 @@ state:
     :linenos:
 
     from urllib.request import urlretrieve
+    import matplotlib.pyplot as plt
 
     gh = "https://raw.githubusercontent.com/OpenMS/pyopenms-docs/master"
     urlretrieve(gh + "/src/data/BSA1.mzML", "BSA1.mzML")
@@ -179,6 +181,7 @@ state:
         use_decreasing_model,
         start_intensity_check,
         False,
+        True
     )
 
     print(e[214].size())

docs/source/img/OpenMS.svg

@@ -12,7 +12,7 @@ Add an entry to ``src/pyOpenMS/pxds/CLASS_NAME.pxd`` with the signature of your
 How can I wrap a new class with pyOpenMS?
 *************************************************
 
-Create a new file ``src/pyOpenMS/pxds/CLASS_NAME.pxd`` and use the `procedure outlined <wrap_classes.rst#how-to-wrap-new-classes>`_. 
+Create a new file ``src/pyOpenMS/pxds/CLASS_NAME.pxd`` and use the `procedure outlined <../community/wrapping_workflows_new_classes.html#how-to-wrap-new-classes>`_. 
 
 
 Can I use multiple output parameters?

docs/source/img/pyOpenMSlogo_color.svg

@@ -31,7 +31,7 @@ can type
 We have binary packages for OSX, Linux and Windows (64 bit only) available from
 `PyPI <https://pypi.org/project/pyopenms>`_. Make sure to download
 the 64bit Python release for Windows. Currently we only support
-Python 3.9, 3.10 and 3.11.
+Python 3.9, 3.10, 3.11, 3.12 and 3.13.
 
 You can install Python first from `here <https://www.python.org/downloads/>`_,
 again make sure to download the 64bit release. You can then open a shell and

docs/source/user_guide/charge_isotope_deconvolution.rst

@@ -27,7 +27,7 @@ analysis.
         masstrace.push_back(p)
 
 Usually, the quantitative features would be produced by a so-called
-:py:class:`~.FeatureFinder` algorithm, which we will discuss in the `next chapter <feature_detection.html>`_. The
+`FeatureFinder` algorithm, which we will discuss in the `next chapter <feature_detection.html>`_. The
 features can be stored in a :py:class:`~.FeatureMap` and written to disk.
 
 .. code-block:: python

docs/source/user_guide/faq.rst

@@ -121,11 +121,14 @@ chromatograms as they are read from the disk. A simple implementation could look
 
 which can the be used as follows:
 
-.. code-block:: output
+.. code-block:: python
 
     filename = b"test.mzML"
     consumer = MSCallback()
     oms.MzMLFile().transform(filename, consumer)
+	
+.. code-block:: output
+	
     Read a spectrum
     Read a spectrum
     Read a spectrum
@@ -138,50 +141,61 @@ spectrum or chromatogram is read from disk, the function ``consumeSpectrum`` or
 ``consumeChromatogram`` is called and a specific action is performed. We can
 use this to implement a simple filtering function for mass spectra:
 
-.. code-block:: output
+.. code-block:: python
 
-    class FilteringConsumer:
-        """
-        Consumer that forwards all calls the internal consumer (after
-        filtering)
-        """
+	import os
+	import pyopenms as oms
+	from urllib.request import urlretrieve
 
-        def __init__(self, consumer, filter_string):
-            self._internal_consumer = consumer
-            self.filter_string = filter_string
+	gh = "https://raw.githubusercontent.com/OpenMS/pyopenms-docs/master"
+	urlretrieve(gh + "/src/data/tiny.mzML", "test.mzML")
 
-        def setExperimentalSettings(self, s):
-            self._internal_consumer.setExperimentalSettings(s)
+	print("Current Working Directory where all files are stored:", os.getcwd())
 
-        def setExpectedSize(self, a, b):
-            self._internal_consumer.setExpectedSize(a, b)
+	class FilteringConsumer:
+		"""
+		Consumer that forwards all calls the internal consumer (after
+		filtering out spectra with less than 'min_spec_size' peaks)
+		"""
 
-        def consumeChromatogram(self, c):
-            if c.getNativeID().find(self.filter_string) != -1:
-                self._internal_consumer.consumeChromatogram(c)
+		def __init__(self, consumer, min_spec_size = 0):
+			self._internal_consumer = consumer
+			self._min_spec_size = min_spec_size
 
-        def consumeSpectrum(self, s):
-            if s.getNativeID().find(self.filter_string) != -1:
-                self._internal_consumer.consumeSpectrum(s)
+		def setExperimentalSettings(self, s):
+			self._internal_consumer.setExperimentalSettings(s)
+
+		def setExpectedSize(self, a, b):
+			self._internal_consumer.setExpectedSize(a, b)
+
+		def consumeChromatogram(self, c):
+			# just forward; do nothing to chromatograms
+			self._internal_consumer.consumeChromatogram(c)
+
+		def consumeSpectrum(self, s):
+			print("Spec has size: ", s.size())
+			if s.size() >= self._min_spec_size:
+				print(" --> keep it")
+				self._internal_consumer.consumeSpectrum(s)
+			else:
+				print(" --> discard it")
 
-    ###################################
-    filter_string = "DECOY"
-    inputfile = "in.mzML"
-    outputfile = "out.mzML"
-    ###################################
 
-    consumer = oms.PlainMSDataWritingConsumer(outputfile)
-    consumer = FilteringConsumer(consumer, filter_string)
+	min_spec_size = 11  ## we will keep spectra with 11 or more peaks and discard the others
+	inputfile = "test.mzML"
+	outputfile = "out.mzML"
 
-    oms.MzMLFile().transform(inputfile, consumer)
+	consumer = oms.PlainMSDataWritingConsumer(outputfile)
+	consumer = FilteringConsumer(consumer, min_spec_size)
 
+	oms.MzMLFile().transform(inputfile, consumer)
 
-where the spectra and chromatograms are filtered by their native ids. It is
+, where the spectra are filtered by their size. It is
 similarly trivial to implement filtering by other attributes. Note how the data
 are written to disk using the :py:class:`~.PlainMSDataWritingConsumer` which is one of
 multiple available consumer classes -- this specific class will simply take the
-spectrum ``s`` or chromatogram ``c`` and write it to disk (the location of the
-output file is given by the ``outfile`` variable).
+:py:class:`~.MSSpectrum` ``s`` or :py:class:`~.MSChromatogram` ``c`` and write it to disk (the location of the
+output file is given by the ``outputfile`` variable).
 
 Note that this approach is memory efficient in cases where computation should
 only occur on part of the data or the whole data may not fit into memory.