def genGraph(infname, outfname, roixmlname=None, roirawname=None, bigGraph=False, outformat="graphml", numfibers=0, centroids=True, **atlases):
"""
Generate a sparse igraph from an MRI file based on input and output names.
Outputs a graphml formatted graph by default
positional args:
================
infname - file name of _fiber.dat file
outfname - Dir+fileName of output .mat file
optional args:
==============
roixmlname - file name of _roi.xml file
roirawname - file name of _roi.raw file
bigGraph - boolean True or False on whether to process a bigraph=True or smallgraph=False
numfibers - the number of fibers
read_shape - **NOTE** Temp arg used compensate for incorrect reading of the adj mat dims
from the xml. Will disappear in future releases.
"""
start = time()
# Determine size of graph to be processed i.e pick a fibergraph module to import
if bigGraph: from fibergraph_bg import FiberGraph
else: from fibergraph_sm import FiberGraph
# If these filenames are undefined then,
# assume that there are ROI files in ../roi
if not(roixmlname and roirawname):
[ inpathname, inbasename ] = os.path.split ( infname )
inbasename = str(inbasename).rpartition ( "_" )[0]
roifp = os.path.normpath ( inpathname + "/../roi/" + inbasename )
roixmlname = roifp + "_roi.xml"
roirawname = roifp + "_roi.raw"
# # Assume that there are mask files in ../mask
# maskfp = os.path.normpath ( inpathname + "/../mask/" + inbasename )
# maskxmlname = maskfp + "_mask.xml"
# maskrawname = maskfp + "_mask.raw"
# Get the ROIs
try:
roix = roi.ROIXML( roixmlname ) # Create object of type ROIXML
rois = roi.ROIData ( roirawname, roix.getShape() )
except:
raise Exception("ROI files not found at: %s, %s" % (roixmlname, roirawname))
# Get the mask
# try:
# maskx = mask.MaskXML( maskxmlname )
# masks = mask.MaskData ( maskrawname, maskx.getShape() )
# except:
# print "Mask files not found at: ", maskxmlname, maskrawname
# sys.exit (-1)
# Create fiber reader
reader = FiberReader( infname )
# Create the graph object
# get dims from reader
fbrgraph = FiberGraph ( reader.shape, rois, None )
print "Parsing MRI studio file {0}".format ( infname )
# Print the high-level fiber information
print(reader)
count = 0
# iterate over all fibers
for fiber in reader:
count += 1
# add the contribution of this fiber to the
fbrgraph.add(fiber)
if numfibers > 0 and count >= numfibers:
break
if count % 10000 == 0:
print ("Processed {0} fibers".format(count) )
#if count == 10000:
#break
del reader
# Done adding edges
fbrgraph.complete(centroids, atlases)
fbrgraph.saveToIgraph(outfname, gformat=outformat)
del fbrgraph
print "\nGraph building complete in %.3f secs" % (time() - start)
return
def genGraph(infname, data_atlas_fn, outfname, bigGraph=False, \
outformat="graphml", numfibers=0, centroids=True, graph_attrs={}, compress=False, **atlases):
"""
Generate a sparse igraph from an MRI file based on input and output names.
We traverse fiber streamlines and map the voxels which they pass through to a brain region, as determined by an atlas. Big graphs have ROIs which are single voxels, whereas small graphs' ROIs are much larger and can be determined either by size, function, or any other method. Outputs a graphml formatted graph by default.
**Positional Arguments**
infname: [.dat; binary file]
- MRIStudio format fiber streamlines.
data_atlas_fn: [.nii; nifti image]
- Region labels which will be used to parcellate the fibers.
**Returns**
outfname: [.graphml; XML file]
- Generated graph from fiber tracts.
**Optional Arguments**
bigGraph: [boolean] (default = False)
- Flag indicating where to process a bigGraph=True or smallGraph=False.
outformat: [string] (default = graphml)
- Requested output format of graph.
numfibers: [int] (default = 0)
- The number of fibers to read/process. If 0, process all fibers.
centroids: [boolean] (default = True)
- Flag indicating whether to add centroids for the graph.
graph_attrs: [dictionary] (default = {})
- Dict with graph attributes to be added with key=attr_name, value=attr_value.
atlases: [dictionary] (default = {})
- Dict with key=atlas_fn, value=region_name_fn
compress: [boolean] (default = False)
- Flag for compressing graphs (useful for processing large quantities of big graphs)
"""
start = time()
# Determine size of graph to be processed i.e pick a fibergraph module to import
if bigGraph: from fibergraph_bg import FiberGraph
else: from fibergraph_sm import FiberGraph
# This ensures there is at least one atlas by default now
if data_atlas_fn not in atlases:
print "Adding default atlas"
atlases[data_atlas_fn] = None
rois = roi.ROIData(data_atlas_fn)
# Create fiber reader
reader = FiberReader(infname)
# DM FIXME: Hacky -- we no longer read shape from fiber file so this happens :-/
reader.shape = rois.data.shape
# Create the graph object
# get dims from reader
fbrgraph = FiberGraph (reader.shape, rois)
print "Parsing MRI studio file {0}".format (infname)
# Print the high-level fiber information
print "Reader Header", reader
count = 0
# iterate over all fibers
for fiber in reader:
count += 1
# add the contribution of this fiber to the
fbrgraph.add(fiber)
if numfibers > 0 and count >= numfibers:
break
if count % 10000 == 0:
print ("Processed {0} fibers".format(count))
del reader
# Done adding edges
fbrgraph.complete(centroids, graph_attrs, atlases)
fbrgraph.saveToIgraph(outfname, gformat=outformat, compress=compress)
del fbrgraph
print "\nGraph building complete in %.3f secs" % (time() - start)
return
