Updated Docs to use PeptideIdentificationList

docs/source/user_guide/PSM_to_features.rst

@@ -45,7 +45,7 @@ Next, load the PeptideIdentifications from an `.idXML` file:
 
 .. code-block:: python
 
-   peptide_ids = []
+   peptide_ids = oms.PeptideIdentificationList()
    protein_ids = []
    oms.IdXMLFile().load(idxml_file, protein_ids, peptide_ids)
 

docs/source/user_guide/export_files_GNPS.rst

@@ -47,7 +47,7 @@ from your :py:class:`~.ConsensusMap` that have no :term:`MS2` spectra annotated.
     filtered_map = oms.ConsensusMap(consensus_map)
     filtered_map.clear(False)
     for feature in consensus_map:
-        if feature.getPeptideIdentifications():
+        if feature.getPeptideIdentifications().size() > 0:
             filtered_map.push_back(feature)
 
     consensusXML_file = "filtered.consensusXML"

docs/source/user_guide/export_pandas_dataframe.rst

@@ -110,7 +110,7 @@ PeptideIdentification
 
     urlretrieve(url + "small.idXML", "small.idXML")
     prot_ids = []
-    pep_ids = []
+    pep_ids = oms.PeptideIdentificationList()
     oms.IdXMLFile().load("small.idXML", prot_ids, pep_ids)
 
     df = oms.peptide_identifications_to_df(pep_ids)

docs/source/user_guide/identification_data.rst

@@ -161,7 +161,8 @@ We can now display the peptides we just stored:
   :linenos:
 
   # Iterate over PeptideIdentification
-  peptide_ids = [peptide_id]
+  peptide_ids = oms.PeptideIdentificationList()
+  peptide_ids.push_back(peptide_id)
   for peptide_id in peptide_ids:
       # Peptide identification values
       print("Peptide ID m/z:", peptide_id.getMZ())
@@ -193,7 +194,7 @@ discussed :ref:`anchor-other-id-data`) which we would do as follows:
   oms.IdXMLFile().store("out.idXML", [protein_id], peptide_ids)
   # and load it back into memory
   prot_ids = []
-  pep_ids = []
+  pep_ids = oms.PeptideIdentificationList()
   oms.IdXMLFile().load("out.idXML", prot_ids, pep_ids)
 
   # Iterate over all protein hits

docs/source/user_guide/interactive_plots.rst

@@ -35,7 +35,7 @@ interactively zoomed-in if you execute the code in a notebook
     exp.updateRanges()
     expandcols = ["RT", "mz", "inty"]
     spectraarrs2d = exp.get2DPeakDataLong(
-        exp.getMinRT(), exp.getMaxRT(), exp.getMinMZ(), exp.getMaxMZ()
+        exp.getMinRT(), exp.getMaxRT(), exp.getMinMZ(), exp.getMaxMZ(), 1
     )
     spectradf = pd.DataFrame(dict(zip(expandcols, spectraarrs2d)))
     spectradf = spectradf.set_index(["RT", "mz"])
@@ -75,17 +75,14 @@ interactively zoomed-in if you execute the code in a notebook
             min_alpha=0,
         )
         .opts(active_tools=["box_zoom"], tools=["hover"], hooks=[new_bounds_hook])
-        .opts(  # weird.. I have no idea why one has to do this. But with one opts you will get an error
-            plot=dict(
-                width=800,
-                height=800,
-                xlabel="Retention time (s)",
-                ylabel="mass/charge (Da)",
-            )
-        )
     )
 
-    hd.dynspread(raster, threshold=0.7, how="add", shape="square")
+hd.dynspread(raster, threshold=0.7, how="add", shape="square").opts(
+    width=800,
+    height=800,
+    xlabel="Retention time (s)",
+    ylabel="mass/charge (Da)",
+)
 
 
 Result:

docs/source/user_guide/other_ms_data_formats.rst

@@ -17,7 +17,7 @@ You can store and load identification data from an `idXML` file as follows:
     gh = gh = "https://raw.githubusercontent.com/OpenMS/pyopenms-docs/master"
     urlretrieve(gh + "/src/data/IdXMLFile_whole.idXML", "test.idXML")
     protein_ids = []
-    peptide_ids = []
+    peptide_ids = oms.PeptideIdentificationList()
     oms.IdXMLFile().load("test.idXML", protein_ids, peptide_ids)
     oms.IdXMLFile().store("test.out.idXML", protein_ids, peptide_ids)
 
@@ -31,7 +31,7 @@ You can store and load identification data from an `mzIdentML` file as follows:
     gh = gh = "https://raw.githubusercontent.com/OpenMS/pyopenms-docs/master"
     urlretrieve(gh + "/src/data/MzIdentML_3runs.mzid", "test.mzid")
     protein_ids = []
-    peptide_ids = []
+    peptide_ids = oms.PeptideIdentificationList()
     oms.MzIdentMLFile().load("test.mzid", protein_ids, peptide_ids)
     oms.MzIdentMLFile().store("test.out.mzid", protein_ids, peptide_ids)
 ..  # alternatively: -- dont do this, doesnt work
@@ -48,7 +48,7 @@ You can store and load identification data from a TPP `pepXML` file as follows:
     gh = gh = "https://raw.githubusercontent.com/OpenMS/pyopenms-docs/master"
     urlretrieve(gh + "/src/data/PepXMLFile_test.pepxml", "test.pepxml")
     protein_ids = []
-    peptide_ids = []
+    peptide_ids = oms.PeptideIdentificationList()
     oms.PepXMLFile().load("test.pepxml", protein_ids, peptide_ids)
     oms.PepXMLFile().store("test.out.pepxml", protein_ids, peptide_ids)
 

docs/source/user_guide/peptide_search.rst

@@ -31,7 +31,7 @@ a fasta database of protein sequences:
     urlretrieve(gh + "/src/data/SimpleSearchEngine_1.mzML", "searchfile.mzML")
     urlretrieve(gh + "/src/data/SimpleSearchEngine_1.fasta", "search.fasta")
     protein_ids = []
-    peptide_ids = []
+    peptide_ids = oms.PeptideIdentificationList()
     oms.SimpleSearchEngineAlgorithm().search(
         "searchfile.mzML", "search.fasta", protein_ids, peptide_ids
     )
@@ -143,9 +143,9 @@ ppm\ (\pm 2\ ppm)`, we expect that we will not find the hit at :math:`775.38` m/
     salgo.setParameters(p)
 
     protein_ids = []
-    peptide_ids = []
+    peptide_ids = oms.PeptideIdentificationList()
     salgo.search("searchfile.mzML", "search.fasta", protein_ids, peptide_ids)
-    print("Found", len(peptide_ids), "peptides")
+    print("Found", peptide_ids.size(), "peptides")
 
 As we can see, using a smaller precursor mass tolerance leads the algorithm to
 find only one hit instead of two. Similarly, if we use the wrong enzyme for
@@ -189,7 +189,7 @@ Now include some additional decoy database generation step as well as subsequent
 
     # Run SimpleSearchAlgorithm, store protein and peptide ids
     protein_ids = []
-    peptide_ids = []
+    peptide_ids = oms.PeptideIdentificationList()
 
     # set some custom search parameters
     simplesearch = oms.SimpleSearchEngineAlgorithm()
@@ -224,7 +224,7 @@ This is done by applying one of the available protein inference algorithms on th
     :linenos:
 
     protein_ids = []
-    peptide_ids = []
+    peptide_ids = oms.PeptideIdentificationList()
 
     # Re-run search since we need to keep decoy hits for inference
     simplesearch.search(searchfile, target_decoy_database, protein_ids, peptide_ids)

docs/source/user_guide/quality_control.rst

@@ -43,7 +43,7 @@ proteomics and metabolomics quality metrics.
     oms.FeatureXMLFile().load("features.featureXML", feature_map)
 
     prot_ids = []  # list of ProteinIdentification()
-    pep_ids = []  # list of PeptideIdentification()
+    pep_ids = oms.PeptideIdentificationList()  # list of PeptideIdentification()
     # OPTIONAL: get protein and peptide identifications from idXML file
     urlretrieve(gh + "/src/data/OpenPepXL_output.idXML", "ids.idXML")
     oms.IdXMLFile().load("ids.idXML", prot_ids, pep_ids)

docs/source/user_guide/untargeted_metabolomics_preprocessing.rst

@@ -141,7 +141,7 @@ Map :term:`MS2` spectra to features as :py:class:`~.PeptideIdentification` objec
             if feature_map.getMetaValue("spectra_data")[
                 0
             ].decode() == exp.getMetaValue("mzML_path"):
-                peptide_ids = []
+                peptide_ids = oms.PeptideIdentificationList()
                 protein_ids = []
                 mapper.annotate(
                     feature_map,
@@ -161,10 +161,10 @@ Map :term:`MS2` spectra to features as :py:class:`~.PeptideIdentification` objec
                     prot_ids.append(prot_id)
                 fm_new.setProteinIdentifications(prot_ids)
                 for feature in feature_map:
-                    pep_ids = []
+                    pep_ids = oms.PeptideIdentificationList()
                     for pep_id in feature.getPeptideIdentifications():
                         pep_id.setIdentifier(f"Identifier_{i}")
-                        pep_ids.append(pep_id)
+                        pep_ids.push_back(pep_id)
                     feature.setPeptideIdentifications(pep_ids)
                     fm_new.push_back(feature)
                 feature_maps_mapped.append(fm_new)

requirements.txt

@@ -6,6 +6,7 @@ scikit-learn
 tabulate
 requests
 bokeh
+jupyter_bokeh
 datashader
 holoviews
 pyviz_comms