Spectrum Normalization
======================

Another very basic mass spectrum processing step is normalization by base peak intensity (the maximum intensity of a mass spectrum).

Let's first load the raw data.

.. code-block:: python
  :linenos:

  from urllib.request import urlretrieve
  import pyopenms as oms
  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",
  )
  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,
  )


Now we apply the normalization.

.. code-block:: python
  :linenos:

  normalizer = oms.Normalizer()
  param = normalizer.getParameters()
  param.setValue("method", "to_one")
  normalizer.setParameters(param)

  normalizer.filterPeakMap(exp)
  plt.bar(
      exp.getSpectrum(0).get_peaks()[0],
      exp.getSpectrum(0).get_peaks()[1],
      snap=False,
  )


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")``.