improve QuantData tutorial

Author: cbielow

docs/source/user_guide/quantitative_data.rst

@@ -6,7 +6,7 @@ features
 
 In OpenMS, information about quantitative data is stored in a so-called
 :py:class:`~.Feature`.  Each
-:py:class:`~.Feature` represents a region in RT and m/z space use for quantitative
+:py:class:`~.Feature` represents a region in RT and m/z for quantitative
 analysis.
 
 .. code-block:: python
@@ -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. The
+:py:class:`~.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
@@ -78,13 +78,13 @@ represented by a :py:class:`~.ConsensusFeature`
 .. code-block:: python
     :linenos:
 
-    feature = oms.ConsensusFeature()
-    feature.setMZ(500.9)
-    feature.setCharge(2)
-    feature.setRT(1500.1)
-    feature.setIntensity(80500)
+    cf = oms.ConsensusFeature()
+    cf.setMZ(500.9)
+    cf.setCharge(2)
+    cf.setRT(1500.1)
+    cf.setIntensity(80500)
 
-    # Generate ConsensusFeature and features from two maps (with id 1 and 2)
+    # Generate ConsensusFeature from features of two maps (with id 1 and 2)
     ### Feature 1
     f_m1 = oms.ConsensusFeature()
     f_m1.setRT(500)
@@ -97,8 +97,8 @@ represented by a :py:class:`~.ConsensusFeature`
     f_m2.setMZ(299.99)
     f_m2.setIntensity(600)
     f_m2.ensureUniqueId()
-    feature.insert(1, f_m1)
-    feature.insert(2, f_m2)
+    cf.insert(1, f_m1)
+    cf.insert(2, f_m2)
 
 We have thus added two features from two individual maps (which have the unique
 identifier ``1`` and ``2``) to the :py:class:`~.ConsensusFeature`.
@@ -110,12 +110,12 @@ the two maps and output the two linked features:
 
     # The two features in map 1 and map 2 represent the same analyte at
     # slightly different RT and m/z
-    for fh in feature.getFeatureList():
+    for fh in cf.getFeatureList():
         print(fh.getMapIndex(), fh.getIntensity(), fh.getRT())
 
-    print(feature.getMZ())
-    feature.computeMonoisotopicConsensus()
-    print(feature.getMZ())
+    print(cf.getMZ())
+    cf.computeMonoisotopicConsensus()
+    print(cf.getMZ())
 
     # Generate ConsensusMap and add two maps (with id 1 and 2)
     cmap = oms.ConsensusMap()
@@ -124,14 +124,14 @@ the two maps and output the two linked features:
     fds[2].filename = "file2"
     cmap.setColumnHeaders(fds)
 
-    feature.ensureUniqueId()
-    cmap.push_back(feature)
+    cf.ensureUniqueId()
+    cmap.push_back(cf)
     oms.ConsensusXMLFile().store("test.consensusXML", cmap)
 
 Inspection of the generated ``test.consensusXML`` reveals that it contains
 references to two :term:`LC-MS/MS` runs (``file1`` and ``file2``) with their respective
 unique identifier. Note how the two features we added before have matching
-unique identifiers.  
+unique identifiers. 
 
 Visualization of the resulting output file reveals a single
 :py:class:`~.ConsensusFeature` of size 2 that links to the two individual features at