--- a/Sphinx/source/plugins/python.rst	Fri Jun 11 09:30:28 2021 +0200
+++ b/Sphinx/source/plugins/python.rst	Fri Jun 11 09:38:15 2021 +0200
@@ -1018,31 +1018,11 @@
 Using slave processes
 .....................
 
-.. highlight:: python
-
 Let us consider the following sample Python script that makes a
-CPU-intensive computation on a REST callback::
-
-  import math
-  import orthanc
-  import time
+CPU-intensive computation on a REST callback:
 
-  # CPU-intensive computation taking about 4 seconds
-  def SlowComputation():
-      start = time.time()
-      for i in range(1000):
-          for j in range(30000):
-              math.sqrt(float(j))
-      end = time.time()
-      duration = (end - start)
-      return 'computation done in %.03f seconds\n' % duration
-
-  def OnRest(output, uri, **request):
-      answer = SlowComputation()
-      output.AnswerBuffer(answer, 'text/plain')
-
-  orthanc.RegisterRestCallback('/computation', OnRest)
-
+.. literalinclude:: python/multiprocessing-1.py
+                    :language: python
 
 .. highlight:: text
 
@@ -1074,8 +1054,6 @@
 the GIL only applies to the Python script: The baseline REST API of
 Orthanc is not affected by the GIL.
 
-.. highlight:: python
-
 The solution is to use the `multiprocessing primitives
 <https://docs.python.org/3/library/multiprocessing.html>`__ of Python.
 The "master" Python interpreter that is initially started by the
@@ -1084,39 +1062,10 @@
 processes running a separate Python interpreter. This allows to
 offload intensive computations from the "master" Python interpreter of
 Orthanc onto those "slave" interpreters. The ``multiprocessing``
-library is actually quite straightforward to use::
-
-  import math
-  import multiprocessing
-  import orthanc
-  import signal
-  import time
+library is actually quite straightforward to use:
 
-  # CPU-intensive computation taking about 4 seconds
-  # (same code as above)
-  def SlowComputation():
-      start = time.time()
-      for i in range(1000):
-          for j in range(30000):
-              math.sqrt(float(j))
-      end = time.time()
-      duration = (end - start)
-      return 'computation done in %.03f seconds\n' % duration
-
-  # Ignore CTRL+C in the slave processes
-  def Initializer():
-      signal.signal(signal.SIGINT, signal.SIG_IGN)
-
-  # Create a pool of 4 slave Python interpreters
-  POOL = multiprocessing.Pool(4, initializer = Initializer)
-
-  def OnRest(output, uri, **request):
-      # Offload the call to "SlowComputation" onto one slave process.
-      # The GIL is unlocked until the slave sends its answer back.
-      answer = POOL.apply(SlowComputation)
-      output.AnswerBuffer(answer, 'text/plain')
-
-  orthanc.RegisterRestCallback('/computation', OnRest)
+.. literalinclude:: python/multiprocessing-2.py
+                    :language: python
 
 .. highlight:: text
 
@@ -1157,22 +1106,11 @@
 You must write your Python plugin so as that all the calls to
 ``orthanc`` are moved from the slaves process to the master
 process. For instance, here is how you would parse a DICOM file in a
-slave process::
-
-  import pydicom
-  import io
+slave process:
 
-  def OffloadedDicomParsing(dicom):
-      # No access to the "orthanc" library here, as we are in the slave process
-      dataset = pydicom.dcmread(io.BytesIO(dicom))
-      return str(dataset)
+.. literalinclude:: python/multiprocessing-3.py
+                    :language: python
 
-  def OnRest(output, uri, **request):
-      # The call to "orthanc.RestApiGet()" is only possible in the master process
-      dicom = orthanc.RestApiGet('/instances/19816330-cb02e1cf-df3a8fe8-bf510623-ccefe9f5/file')
-      answer = POOL.apply(OffloadedDicomParsing, args = (dicom, ))
-      output.AnswerBuffer(answer, 'text/plain')
-      
 Communication primitives such as ``multiprocessing.Queue`` are
 available to exchange messages from the "slave" Python interpreters to
 the "master" Python interpreter for more advanced scenarios.
@@ -1184,28 +1122,7 @@
 the REST API of Orthanc (without have to set credentials in your
 plugin). Any HTTP client library for Python, such as `requests
 <https://requests.readthedocs.io/en/master/>`__, can then be used to
-access the REST API of Orthanc. Here is a minimal example::
+access the REST API of Orthanc. Here is a minimal example:
 
-  import json
-  import multiprocessing
-  import orthanc
-  import requests
-  import signal
-  
-  TOKEN = orthanc.GenerateRestApiAuthorizationToken()
-  
-  def SlaveProcess():
-      r = requests.get('http://localhost:8042/instances',
-                       headers = { 'Authorization' : TOKEN })
-      return json.dumps(r.json())
-  
-  def Initializer():
-      signal.signal(signal.SIGINT, signal.SIG_IGN)
-  
-  POOL = multiprocessing.Pool(4, initializer = Initializer)
-  
-  def OnRest(output, uri, **request):
-      answer = POOL.apply(SlaveProcess)
-      output.AnswerBuffer(answer, 'text/plain')
-  
-  orthanc.RegisterRestCallback('/computation', OnRest)
+.. literalinclude:: python/multiprocessing-4.py
+                    :language: python