fix PSM_to_features tutorial

docs/source/user_guide/PSM_to_features.rst

 Feature Map Annotation with Peptide Identifications 
 =================================================== 
 A :term:`feature map` is usually obtained from running a :term:`feature finder`, e.g. :py:class:`~.FeatureFinderAlgorithmPicked` (see `Feature Detection <feature_detection.html>`_). 
 A logical next step is to compare features across runs using `Map Alignment <map_alignment.html>`_ and `Feature Linking <feature_linking.html>`_. 
 However, most map aligners in pyOpenMS require features which are annotated with PSMs (see `Identication Data <identification_data.html>`_). 
 To link features to their respective PSMs (as obtained from a search engine, such as Comet), we can use the :py:class:`~.IDMapper`. 
 Step 0: Download Example data 
 ----------------------------- 
 .. code-block:: python 
     :linenos: 
     import pyopenms as oms 
     from urllib.request import urlretrieve 
     base_url = ( 
         "https://raw.githubusercontent.com/OpenMS/pyopenms-docs/master/src/data/" 
     ) 
     feature_file = "BSA1_F1.featureXML" 
     urlretrieve(base_url + feature_file, feature_file) 
     idxml_file = "BSA1_F1.idXML" 
     urlretrieve(base_url + idxml_file, idxml_file) 
 Step 1: Load the Feature Map 
 ---------------------------- 
 First, load the FeatureMap from a `.featureXML` file: 
 .. code-block:: python 
    import pyopenms as oms 
    feature_map = oms.FeatureMap() 
    oms.FeatureXMLFile().load(feature_file, feature_map) 
 Step 2: Load Peptide Identifications 
 ------------------------------------ 
 Next, load the PeptideIdentifications from an `.idXML` file: 
 .. code-block:: python 
    peptide_ids = [] 
    protein_ids = [] 
    oms.IdXMLFile().load(idxml_file, protein_ids, peptide_ids) 
 Step 3: Initialize and Configure `IDMapper` 
 ------------------------------------------- 
 Now, configure `IDMapper` to apply **retention time (RT) and m/z tolerance settings**: 
 .. code-block:: python 
    id_mapper = oms.IDMapper() 
    params = id_mapper.getParameters() 
    params.setValue("rt_tolerance", 5.0)  # RT tolerance in seconds 
    params.setValue("mz_tolerance", 10.0)  # m/z tolerance in ppm 
    id_mapper.setParameters(params) 
 Step 4: Annotate the FeatureMap 
 ------------------------------- 
 Use the configured `IDMapper` to link peptide IDs to the FeatureMap: 
 .. code-block:: python 
    # annotate() can optionally use the underlying raw MS data (spectra) to annotate unidentified MS/MS scans to features in the FeatureMap 
    # We don't need this here, so we provide an empty default. 
    spectra = oms.MSExperiment() 
    id_mapper.annotate(feature_map, peptide_ids, protein_ids, True, True, spectra) 
 Step 5: Save the Annotated FeatureMap 
 -------------------------------------- 
 Finally, store the modified FeatureMap back to a file: 
 .. code-block:: python 
    oms.FeatureXMLFile().store("BSA1_F1_annotated.featureXML", feature_map) 
 .. tip:: 
    You can visualize the annotated FeatureMap using OpenMS visualization tools like `TOPPView`. 
 You have successfully **annotated a FeatureMap** with PeptideIdentifications using `IDMapper`. This allows further downstream analysis in (py)OpenMS workflows. 
@@ -69,7 +69,10 @@ Use the configured `IDMapper` to link peptide IDs to the FeatureMap:
 
 .. code-block:: python
 
-   id_mapper.annotate(feature_map, peptide_ids, protein_ids, use_centroid_rt=True, use_centroid_mz=True, spectra=None)
+   # annotate() can optionally use the underlying raw MS data (spectra) to annotate unidentified MS/MS scans to features in the FeatureMap
+   # We don't need this here, so we provide an empty default.
+   spectra = oms.MSExperiment()
+   id_mapper.annotate(feature_map, peptide_ids, protein_ids, True, True, spectra)
 
 Step 5: Save the Annotated FeatureMap
 --------------------------------------