def write_texture(tex_data, filename, intent=None, meta_data=None):
""" Write the n-dimensional numpy array 'text_data' into filename
using gifti (.gii) or tex format.
"""
if tex_data.dtype == np.bool:
tex_data = tex_data.astype(np.int16)
if has_ext_gzsafe(filename, 'gii'):
#tex = np.arange(len(cor), dtype=int)
if intent is None:
if np.issubdtype(tex_data.dtype, np.int):
intent = 'NIFTI_INTENT_LABEL'
dtype = None
elif np.issubdtype(tex_data.dtype, np.float):
intent = 'NIFTI_INTENT_NONE'
#intent = 'dimensionless' #?fg
#dtype = 'NIFTI_TYPE_FLOAT32'
tex_data.astype(np.float32)
if pyhrf.cfg['global']['write_texture_minf']:
s = "attributes = {'data_type' : 'FLOAT'}"
f = open(filename + '.minf', 'w')
f.write(s)
f.close()
else:
raise NotImplementedError("Unsupported dtype %s" \
%str(tex_data.dtype))
gii_array = gifti.GiftiDataArray.from_array(tex_data, intent)
if meta_data is not None:
md = {'pyhrf_cuboid_data':meta_data}
gmeta_data = gifti.GiftiMetaData.from_dict(md)
else:
gmeta_data = None
tex_gii = gifti.GiftiImage(darrays=[gii_array,], meta=gmeta_data)
pyhrf.verbose(3, 'Write texture to %s'%filename)
gifti.write(tex_gii, filename)
elif has_ext_gzsafe(filename, 'tex'):
if meta_data is not None:
print 'Warning: meta ignored when saving to tex format'
from pyhrf.tools.io.tio import Texture
tex = Texture(filename, data=tex_data)
tex.write()
else:
raise NotImplementedError('Unsupported extension for file %s' \
%filename)
def read_texture(tex):
if has_ext_gzsafe(tex, 'gii'):
#from gifti import loadImage
#texture = loadImage(tex).arrays[0].data
tex_gii = gifti.read(tex)
if len(tex_gii.darrays) > 1: #2D texture ... #TODO: check
texture = np.vstack([a.data for a in tex_gii.darrays])
else:
texture = tex_gii.darrays[0].data
return texture, tex_gii
elif has_ext_gzsafe(tex, 'tex'):
from pyhrf.tools.io.tio import Texture
texture = Texture.read(tex).data
return texture, None
else:
raise NotImplementedError('Unsupported %s extension' \
%op.splitext(tex)[1])
def test_single_surface_PFPS_ML(self):
"""PF estimation method : path sampling. ML on p(label|beta).
topology from a surfacic RDI
"""
# generate a field:
beta = 0.4
nbClasses = 2
print 'generating potts ..., beta =', beta
# grab surfacic data:
from pyhrf.graph import graph_from_mesh, sub_graph, graph_is_sane
from pyhrf.tools.io.tio import Texture
from soma import aims
print 'import done'
roiId = 20
mfn = pyhrf.get_data_file_name('right_hemisphere.mesh')
print 'mesh file:', mfn
mesh = aims.read(mfn)
print 'mesh read'
triangles = [t.arraydata() for t in mesh.polygon().list()]
print 'building graph ... '
wholeGraph = graph_from_mesh(triangles)
roiMaskFile = pyhrf.get_data_file_name('roimask_gyrii_tuned.tex')
roiMask = Texture.read(roiMaskFile).data.astype(int)
mroi = np.where(roiMask==roiId)
g, nm = sub_graph(wholeGraph, mroi[0])
print "g:", len(g), len(g[0])
nnodes = len(g)
points = np.vstack([v.arraydata() for v in mesh.vertex().list()])
weights = [[1./dist(points[j],points[k]) for k in g[j]]
for j in xrange(nnodes)]
print "weights:", len(weights), len(weights[0])
if 1:
for j in xrange(nnodes):
s = sum(weights[j]) * 1.
for k in xrange(len(weights[j])):
weights[j][k] = weights[j][k]/s * len(weights[j])
labels = genPotts(g, beta, nbClasses, weights=weights)
print labels
# partition function estimation
gridLnz = Cpt_Vec_Estim_lnZ_Graph(g, nbClasses,
GraphWeight=weights)
print 'gridLnz with weights:'
print gridLnz
# beta estimation
be, pb = beta_estim_obs_field(g, labels, gridLnz, 'ML', weights)
print 'betaML:', be
weights = None
gridLnz = Cpt_Vec_Estim_lnZ_Graph(g, nbClasses,
GraphWeight=weights)
print 'gridLnz without weights:'
print gridLnz
# beta estimation
be, pb = beta_estim_obs_field(g, labels, gridLnz, 'ML', weights)
print 'betaML:', be
gridPace = gridLnz[1][1] - gridLnz[1][0]
assert abs(be-beta) <= gridPace
