Changes between Initial Version and Version 1 of Examples/Brain

Timestamp:

04/11/17 11:28:49 (7 years ago)

Author:

Herwig Zilken

Comment:

--

Examples/Brain

@@ +1 @@
+= Animated movie of neuroscience brain data with ParaView
+[[PageOutline]]
+
+== Background
+At FZJ, the institute for [http://www.fz-juelich.de/inm/inm-1/EN/Home/home_node.html structural and functional organisation of the brain (INM-1)] develops a 3-D model of the human brain which considers cortical architecture, connectivity, genetics and function. The INM-1 research group [http://www.fz-juelich.de/inm/inm-1/EN/Forschung/Fibre%20Architecture/Fibre%20Architecture_node.html Fiber Architecture] develops techniques to reconstruct the three-dimensional nerve fiber architecture in mouse, rat, monkey, and human brains at microscopic resolution. As a key technology, the neuroimaging technique Three-dimensional Polarized Light Imaging (3D-PLI) is used. To determine the spatial orientations of the nerve fibers, a fixated and frozen postmortem brain is cut with a cryotome into histological sections (≤ 70 µm). Every slice is then scanned by high resolution microscopes.
+
+== Data
+The datset used in this visualisation scenario consists of 234 slices of gridsize 31076x28721, resulting in a rectilinear uniform grid of size 31076x28721x234, ~200 GB memory usage in total. The data was stored as raw binary unsigned char data, one file for each slice.
+
+== Conversion to HDF5
+Because !ParaView has a very decent XDMF/HDF5 reader, we decided to convert the raw data to hdf5 first.
+This was done using a Python script. Before Python can be used on our JURECA cluster, the necessary modules have to be loaded first:
+{{{
+module load GCC/5.4.0
+module load ParaStationMPI/5.1.5-1
+module load h5py/2.6.0-Python-2.7.12
+module load HDF5/1.8.17
+}}}
+
+In the Python-script, the directory containing the 234 slice files is scanned for the filenames. Every file is opened and the raw content is read into a numpy array. This numpy array is written into a hdf5 file, which was created first. 
+
+{{{
+#!python
+import sys
+import h5py # http://www.h5py.org/
+import numpy as np
+import glob
+
+dir = "/homeb/zam/zilken/JURECA/projekte/hdf5_inm_converter/Vervet_Sehrinde_rightHem_direction/data"
+hdf5Filename="/homeb/zam/zilken/JURECA/projekte/hdf5_inm_converter/Vervet_Sehrinde_rightHem_direction/data/Vervet_Sehrinde.h5"
+
+# grid-size of one slice
+numX = 28721
+numY = 31076
+
+#scan directory for filenames
+files = glob.glob(dir + "/*.raw")
+numSlices = len(files) # actually 234 slices for this specific dataset
+
+# create hdf5 file
+fout = h5py.File(hdf5Filename, 'w')
+# create a dataset in the hdf5 file of type unsigned char = uint8
+dset = fout.create_dataset("PLI", (numSlices, numX, numY), dtype=np.uint8)
+
+i = 0
+for rawFilename in sorted(files):
+   print "processing " + rawFilename
+   sys.stdout.flush()
+
+   # open each raw file
+   fin = open(rawFilename, "rb")
+   # and read the content
+   v = np.fromfile(fin, dtype=np.uint8, count=numX*numY)
+   fin.close()
+   v = v.reshape(1, numX, numY)
+   # store the data in the hdf5 file at the right place
+   dset[i, : , :]=v
+
+
+print "success"
+fout.close()
+
+}}}
+
+== Creating XDMF Files
+