.. AUTO-GENERATED FILE -- DO NOT EDIT!

.. _example_reconst_csd_parallel:


=================================
Parallel reconstruction using CSD
=================================

This example shows how to use parallelism (multiprocessing) using
``peaks_from_model`` in order to speedup the signal reconstruction
process. For this example will we use the same initial steps
as we used in :ref:`example_reconst_csd`.

Import modules, fetch and read data, apply the mask and calculate the response
function.

::
  
  import multiprocessing
  
  from dipy.data import fetch_stanford_hardi, read_stanford_hardi
  
  fetch_stanford_hardi()
  img, gtab = read_stanford_hardi()
  data = img.get_data()
  
  from dipy.segment.mask import median_otsu
  
  maskdata, mask = median_otsu(data, 3, 1, False,
                               vol_idx=range(10, 50), dilate=2)
  
  from dipy.reconst.csdeconv import auto_response
  
  response, ratio = auto_response(gtab, maskdata, roi_radius=10, fa_thr=0.7)
  
  data = maskdata[:, :, 33:37]
  mask = mask[:, :, 33:37]
  

Now we are ready to import the CSD model and fit the datasets.

::
  
  from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
  
  csd_model = ConstrainedSphericalDeconvModel(gtab, response)
  
  from dipy.data import get_sphere
  
  sphere = get_sphere('symmetric724')
  

Compute the CSD-based ODFs using ``peaks_from_model``. This function has a
parameter called ``parallel`` which allows for the voxels to be processed in
parallel. If ``nbr_processes`` is None it will figure out automatically the
number of CPUs available in your system. Alternatively, you can set
``nbr_processes`` manually. Here, we show an example where we compare the
duration of execution with or without parallelism.

::
  
  import time
  from dipy.direction import peaks_from_model
  
  start_time = time.time()
  csd_peaks_parallel = peaks_from_model(model=csd_model,
                                        data=data,
                                        sphere=sphere,
                                        relative_peak_threshold=.5,
                                        min_separation_angle=25,
                                        mask=mask,
                                        return_sh=True,
                                        return_odf=False,
                                        normalize_peaks=True,
                                        npeaks=5,
                                        parallel=True,
                                        nbr_processes=None)
  
  time_parallel = time.time() - start_time
  print("peaks_from_model using " + str(multiprocessing.cpu_count())
        + " process ran in :" + str(time_parallel) + " seconds")
  

``peaks_from_model`` using 8 processes ran in :114.425682068 seconds

::
  
  start_time = time.time()
  csd_peaks = peaks_from_model(model=csd_model,
                               data=data,
                               sphere=sphere,
                               relative_peak_threshold=.5,
                               min_separation_angle=25,
                               mask=mask,
                               return_sh=True,
                               return_odf=False,
                               normalize_peaks=True,
                               npeaks=5,
                               parallel=False,
                               nbr_processes=None)
  
  time_single = time.time() - start_time
  print("peaks_from_model ran in :" + str(time_single) + " seconds")
  

``peaks_from_model`` ran in :242.772505999 seconds

::
  
  print("Speedup factor : " + str(time_single / time_parallel))
  

Speedup factor : 2.12166099088

In Windows if you get a runtime error about frozen executable please start
your script by adding your code above in a ``main`` function and use:

if __name__ == '__main__':
    import multiprocessing
    multiprocessing.freeze_support()
    main()

        
.. admonition:: Example source code

   You can download :download:`the full source code of this example <./reconst_csd_parallel.py>`.
   This same script is also included in the dipy source distribution under the
   :file:`doc/examples/` directory.

