def load_and_get_fdata_params(self):
pyhrf.verbose(1,'Load mask from: %s' %self.mask_file)
if self.data_type == 'surface':
pyhrf.verbose(2,'Read mesh from: %s' %self.mesh_file)
p = {'mask' : xndarray.load(self.mask_file).data}
pyhrf.verbose(1, 'Mask shape %s' %str(p['mask'].shape))
if self.data_type == 'surface':
p['graph'] = graph_from_mesh(read_mesh(self.mesh_file))
return p
def extract_sub_mesh(cor, tri, center_node, radius):
g = graph_from_mesh(tri)
subg, kept_nodes = bfs_sub_graph(g, center_node, radius)
subcor = cor[kept_nodes]
n_idx = np.zeros(len(g), dtype=int)
keep = np.zeros(len(g), dtype=bool)
keep[kept_nodes] = True
n_idx[kept_nodes] = range(len(kept_nodes))
new_tri = np.vstack([n_idx[t] for t in tri if keep[t].all()])
# new_tri = []
# for t in tri.data:
# if keep[t].all():
# new_tri.append(n_idx[t])
#new_tri = np.vstack(new_tri)
return subcor, new_tri
def extract_sub_mesh(cor, tri, center_node, radius):
g = graph_from_mesh(tri)
subg, kept_nodes = bfs_sub_graph(g, center_node, radius)
subcor = cor[kept_nodes]
n_idx = np.zeros(len(g), dtype=int)
keep = np.zeros(len(g), dtype=bool)
keep[kept_nodes] = True
n_idx[kept_nodes] = range(len(kept_nodes))
new_tri = np.vstack([n_idx[t] for t in tri if keep[t].all()])
# new_tri = []
# for t in tri.data:
# if keep[t].all():
# new_tri.append(n_idx[t])
#new_tri = np.vstack(new_tri)
return subcor, new_tri
def load_fmri_surf_data(boldFiles, meshFile, roiMaskFile=None):
""" Load FMRI BOLD surface data from files.
Arguments:
boldFiles -- pathes to 2D texture data files (ie multisession data).
roiMaskFile -- a path to a 1D texture data file.
meshFile -- a path to a mesh file
Return:
A tuple (graphs, bolds, sessionScans, roiMask, dataHeader)
graphs -- a dict {roiId:roiGraph}. Each roiGraph is a list of neighbours list
bold -- a dict {roiId:bold}. Each bold is a 2D numpy float array
with axes [time,position]
sessionScans -- a list of scan indexes for every session
roiMask -- a 1D numpy int array defining ROIs (0 stands for the background)
dataHeader -- the header from the BOLD data file
"""
#Load ROIs:
pyhrf.verbose(1, 'load roi mask: ' + roiMaskFile)
#roiMask = Texture.read(roiMaskFile).data.astype(int)
roiMask,_ = read_texture(roiMaskFile)
pyhrf.verbose(1, 'roi mask shape: ' + str(roiMask.shape))
#Load BOLD:
lastScan = 0
sessionScans = []
bolds = []
for boldFile in boldFiles:
pyhrf.verbose(1, 'load bold: ' + boldFile)
b,_ = read_texture(boldFile)
pyhrf.verbose(1, 'bold shape: ' + str(b.shape))
bolds.append(b)
sessionScans.append(np.arange(lastScan, lastScan+b.shape[0], dtype=int))
lastScan += b.shape[0]
bold = np.concatenate(tuple(bolds))
# discard bad data (bold with var=0 and nan values):
discard_bad_data(bold, roiMask, time_axis=0)
#Load mesh:
pyhrf.verbose(1, 'load mesh: ' + meshFile)
coords,triangles,coord_sys = read_mesh(meshFile)
#from soma import aims
# mesh = aims.read(meshFile)
# triangles = [t.arraydata() for t in mesh.polygon().list()]
pyhrf.verbose(3, 'building graph ... ')
wholeGraph = graph_from_mesh(triangles)
assert graph_is_sane(wholeGraph)
pyhrf.verbose(1, 'Mesh has %d nodes' %len(wholeGraph))
assert len(roiMask) == len(wholeGraph)
assert bold.shape[1] == len(wholeGraph)
pyhrf.verbose(3, 'Computing length of edges ... ')
edges_l = np.array([np.array([distance(coords[i],coords[n],coord_sys.xform) \
for n in nl]) \
for i,nl in enumerate(wholeGraph)], dtype=object)
#Split bold and graph into rois:
roiBold = {}
graphs = {}
edge_lengthes = {}
for roiId in np.unique(roiMask):
mroi = np.where(roiMask==roiId)
g, nm = sub_graph(wholeGraph, mroi[0])
edge_lengthes[roiId] = edges_l[mroi]
graphs[roiId] = g
roiBold[roiId] = bold[:, mroi[0]].astype(np.float32)
return graphs, roiBold, sessionScans, roiMask, edge_lengthes
def load_surf_bold_mask(bold_files, mesh_file, mask_file=None):
from pyhrf.tools._io import read_mesh, read_texture, discard_bad_data
logger.info('Load mesh: ' + mesh_file)
coords, triangles, coord_sys = read_mesh(mesh_file)
logger.debug('Build graph...')
fgraph = graph_from_mesh(triangles)
assert graph_is_sane(fgraph)
logger.info('Mesh has %d nodes', len(fgraph))
logger.debug('Compute length of edges ... ')
edges_l = np.array([np.array([distance(coords[i],
coords[n],
coord_sys.xform)
for n in nl])
for i, nl in enumerate(fgraph)], dtype=object)
if mask_file is None or not op.exists(mask_file):
logger.warning('Mask file %s does not exist. Taking '
'all nodes ...', mask_file)
mask = np.ones(len(fgraph))
mask_meta_obj = None
mask_loaded_from_file = False
else:
mask, mask_meta_obj = read_texture(mask_file)
mask_loaded_from_file = True
if not (np.round(mask) == mask).all():
raise Exception("Mask is not n-ary")
if len(mask) != len(fgraph):
raise Exception('Size of mask (%d) is different from size '
'of graph (%d)' % (len(mask), len(fgraph)))
mask = mask.astype(np.int32)
if mask.min() == -1:
mask += 1
# Split graph into rois:
graphs = {}
edge_lengths = {}
for roi_id in np.unique(mask):
mroi = np.where(mask == roi_id)
graph, _ = sub_graph(fgraph, mroi[0])
edge_lengths[roi_id] = edges_l[mroi]
graphs[roi_id] = graph
# Load BOLD:
last_scan = 0
session_scans = []
bolds = []
for bold_file in bold_files:
logger.info('load bold: %s', bold_file)
bold, _ = read_texture(bold_file)
logger.info('bold shape: %s', str(bold.shape))
bolds.append(bold)
session_scans.append(np.arange(last_scan, last_scan + bold.shape[0],
dtype=int))
last_scan += bold.shape[0]
bold = np.concatenate(tuple(bolds))
if len(fgraph) != bold.shape[1]:
raise Exception('Nb positions not consistent between BOLD (%d) '
'and mesh (%d)' % (bold.shape[1], len(fgraph)))
# discard bad data (bold with var=0 and nan values):
discard_bad_data(bold, mask, time_axis=0)
return mask, mask_meta_obj, mask_loaded_from_file, bold, session_scans, \
graphs, edge_lengths
def load_surf_bold_mask(bold_files, mesh_file, mask_file=None):
from pyhrf.tools._io import read_mesh, read_texture, discard_bad_data
logger.info('Load mesh: ' + mesh_file)
coords, triangles, coord_sys = read_mesh(mesh_file)
logger.debug('Build graph...')
fgraph = graph_from_mesh(triangles)
assert graph_is_sane(fgraph)
logger.info('Mesh has %d nodes', len(fgraph))
logger.debug('Compute length of edges ... ')
edges_l = np.array([
np.array([distance(coords[i], coords[n], coord_sys.xform) for n in nl])
for i, nl in enumerate(fgraph)
],
dtype=object)
if mask_file is None or not op.exists(mask_file):
logger.warning('Mask file %s does not exist. Taking '
'all nodes ...', mask_file)
mask = np.ones(len(fgraph))
mask_meta_obj = None
mask_loaded_from_file = False
else:
mask, mask_meta_obj = read_texture(mask_file)
mask_loaded_from_file = True
if not (np.round(mask) == mask).all():
raise Exception("Mask is not n-ary")
if len(mask) != len(fgraph):
raise Exception('Size of mask (%d) is different from size '
'of graph (%d)' % (len(mask), len(fgraph)))
mask = mask.astype(np.int32)
if mask.min() == -1:
mask += 1
# Split graph into rois:
graphs = {}
edge_lengths = {}
for roi_id in np.unique(mask):
mroi = np.where(mask == roi_id)
graph, _ = sub_graph(fgraph, mroi[0])
edge_lengths[roi_id] = edges_l[mroi]
graphs[roi_id] = graph
# Load BOLD:
last_scan = 0
session_scans = []
bolds = []
for bold_file in bold_files:
logger.info('load bold: %s', bold_file)
bold, _ = read_texture(bold_file)
logger.info('bold shape: %s', str(bold.shape))
bolds.append(bold)
session_scans.append(
np.arange(last_scan, last_scan + bold.shape[0], dtype=int))
last_scan += bold.shape[0]
bold = np.concatenate(tuple(bolds))
if len(fgraph) != bold.shape[1]:
raise Exception('Nb positions not consistent between BOLD (%d) '
'and mesh (%d)' % (bold.shape[1], len(fgraph)))
# discard bad data (bold with var=0 and nan values):
discard_bad_data(bold, mask, time_axis=0)
return mask, mask_meta_obj, mask_loaded_from_file, bold, session_scans, \
graphs, edge_lengths
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
def load_fmri_surf_data(boldFiles, meshFile, roiMaskFile=None):
""" Load FMRI BOLD surface data from files.
Arguments:
boldFiles -- pathes to 2D texture data files (ie multisession data).
roiMaskFile -- a path to a 1D texture data file.
meshFile -- a path to a mesh file
Return:
A tuple (graphs, bolds, sessionScans, roiMask, dataHeader)
graphs -- a dict {roiId:roiGraph}. Each roiGraph is a list of neighbours list
bold -- a dict {roiId:bold}. Each bold is a 2D numpy float array
with axes [time,position]
sessionScans -- a list of scan indexes for every session
roiMask -- a 1D numpy int array defining ROIs (0 stands for the background)
dataHeader -- the header from the BOLD data file
"""
from pyhrf.graph import graph_from_mesh, sub_graph, graph_is_sane
# Load ROIs:
logger.info('load roi mask: %s', roiMaskFile)
#roiMask = Texture.read(roiMaskFile).data.astype(int)
roiMask, _ = read_texture(roiMaskFile)
logger.info('roi mask shape: %s', str(roiMask.shape))
# Load BOLD:
lastScan = 0
sessionScans = []
bolds = []
for boldFile in boldFiles:
logger.info('load bold: %s', boldFile)
b, _ = read_texture(boldFile)
logger.info('bold shape: %s', str(b.shape))
bolds.append(b)
sessionScans.append(
np.arange(lastScan, lastScan + b.shape[0], dtype=int))
lastScan += b.shape[0]
bold = np.concatenate(tuple(bolds))
# discard bad data (bold with var=0 and nan values):
discard_bad_data(bold, roiMask, time_axis=0)
# Load mesh:
logger.info('load mesh: %s', meshFile)
coords, triangles, coord_sys = read_mesh(meshFile)
#from soma import aims
# mesh = aims.read(meshFile)
# triangles = [t.arraydata() for t in mesh.polygon().list()]
logger.info('building graph ... ')
wholeGraph = graph_from_mesh(triangles)
assert graph_is_sane(wholeGraph)
logger.info('Mesh has %d nodes', len(wholeGraph))
assert len(roiMask) == len(wholeGraph)
assert bold.shape[1] == len(wholeGraph)
logger.info('Computing length of edges ... ')
edges_l = np.array([np.array([distance(coords[i], coords[n], coord_sys.xform)
for n in nl])
for i, nl in enumerate(wholeGraph)], dtype=object)
# Split bold and graph into rois:
roiBold = {}
graphs = {}
edge_lengthes = {}
for roiId in np.unique(roiMask):
mroi = np.where(roiMask == roiId)
g, nm = sub_graph(wholeGraph, mroi[0])
edge_lengthes[roiId] = edges_l[mroi]
graphs[roiId] = g
roiBold[roiId] = bold[:, mroi[0]].astype(np.float32)
return graphs, roiBold, sessionScans, roiMask, edge_lengthes