Update spectrum_normalization.rst (#424)

This commit enhances the 'Spectrum Normalization' section of the documentation. The changes include:
-Images depicting raw and normalized spectra have been added to provide a clearer understanding of the normalization process.
-The text has been extended with explanations of the normalization techniques and their importance in mass spectrometry data analysis.
-The images have been added to the 'img' directory and are referenced in the documentation using relative paths.
Please review the updates for technical accuracy and clarity.

docs/source/user_guide/spectrum_normalization.rst

@@ -1,9 +1,12 @@
 Spectrum Normalization
 ======================
 
-Another very basic mass spectrum processing step is normalization by base peak intensity (the maximum intensity of a mass spectrum).
+Normalization by base peak intensity is a fundamental processing step in mass spectrometry. This method scales the peak intensities in a spectrum such that the highest peak reaches a maximum value, typically set to one. This approach facilitates the comparison of different spectra by standardizing the intensity scale.
 
-Let's first load the raw data.
+Loading the Raw Data
+--------------------
+
+To begin, we need to load the mass spectrometry data. The following Python code demonstrates how to load a spectrum from an mzML file using the pyOpenMS library.
 
 .. code-block:: python
   :linenos:
@@ -13,20 +16,22 @@ Let's first load the raw data.
   import matplotlib.pyplot as plt
 
   gh = "https://raw.githubusercontent.com/OpenMS/pyopenms-docs/master"
-  urlretrieve(
-      gh + "/src/data/peakpicker_tutorial_1_baseline_filtered.mzML",
-      "tutorial.mzML",
-  )
+  urlretrieve(gh + "/src/data/peakpicker_tutorial_1_baseline_filtered.mzML", "tutorial.mzML")
+
   exp = oms.MSExperiment()
   oms.MzMLFile().load("tutorial.mzML", exp)
-  plt.bar(
-      exp.getSpectrum(0).get_peaks()[0],
-      exp.getSpectrum(0).get_peaks()[1],
-      snap=False,
-  )
 
+  plt.bar(exp.getSpectrum(0).get_peaks()[0], exp.getSpectrum(0).get_peaks()[1], snap=False)
+  plt.show()
+
+.. image:: img/before_normalization.png
+   :align: center
+   :alt: Raw spectrum before normalization
 
-Now we apply the normalization.
+Normalization Procedure
+-----------------------
+
+After loading the data, the next step is to apply normalization.
 
 .. code-block:: python
   :linenos:
@@ -37,13 +42,27 @@ Now we apply the normalization.
   normalizer.setParameters(param)
 
   normalizer.filterPeakMap(exp)
-  plt.bar(
-      exp.getSpectrum(0).get_peaks()[0],
-      exp.getSpectrum(0).get_peaks()[1],
-      snap=False,
-  )
+  plt.bar(exp.getSpectrum(0).get_peaks()[0], exp.getSpectrum(0).get_peaks()[1], snap=False)
+  plt.show()
+
+.. image:: img/after_normalization.png
+   :align: center
+   :alt: Spectrum after normalization
+
+TIC Normalization
+-----------------
 
+Another approach to normalization is using the Total Ion Count (TIC). This method adjusts the intensities so that their total sum equals 1.0 in each mass spectrum.
+
+.. code-block:: python
+  :linenos:
+
+  param.setValue("method", "to_TIC")
+  normalizer.setParameters(param)
+  normalizer.filterPeakMap(exp)
+  plt.bar(exp.getSpectrum(0).get_peaks()[0], exp.getSpectrum(0).get_peaks()[1], snap=False)
+  plt.show()
 
-Another way of normalizing is by TIC (total ion count) of the mass spectrum, which scales intensities
-so they add up to :math:`1.0` in each mass spectrum.
-Try it out for yourself by setting: ``param.setValue("method", "to_TIC")``.
+.. image:: img/after_normalization_TIC.png
+   :align: center
+   :alt: Spectrum after TIC normalization