def __init__(self, parameters=None, xmlHandler=NumpyXMLHandler(), xmlLabel=None, xmlComment=None):
XMLParamDrivenClass.__init__(self, parameters, xmlHandler, xmlLabel, xmlComment)
clabel = self.parameters["name"]
ad = self.parameters["domains"]
an = self.parameters["orientation"]
vlab = self.parameters["value_label"]
fl = self.parameters["dataFiles"]
eaxes = self.parameters["explodedAxes"]
self.outDir = "./"
# print ''
# print 'clabel:', clabel
# print 'eaxes :', eaxes
# print 'an:', an
if len(fl) > 0:
ed = dict(zip(eaxes, [ad[ea] for ea in eaxes]))
if len(eaxes) > 0:
iaxes = list(set(an).difference(eaxes))
else:
iaxes = an
idomains = dict(zip(iaxes, [ad[ia] for ia in iaxes]))
ctree = {}
if 0:
print "fl:", fl
print "id:", idomains
print "iaxes:", iaxes
print "ad:", ad
print "ed:", ed
print "eaxes:", eaxes
if len(eaxes) > 0:
for fn in fl:
n = os.path.splitext(os.path.basename(fn))[0]
blabels, bvals = self.slice_idx_from_filename(n, clabel)
if 0:
print "blabels:", blabels
print "bvals:", bvals
print "fn:", fn
cdata = self.load_cuboid(fn, iaxes, vlab, idomains)
# print 'ctree:'
# print ctree
# print 'cdata:', cdata
set_leaf(ctree, bvals, cdata)
self.cuboid = tree_to_cuboid(ctree, branchLabels=blabels)
else:
self.cuboid = self.load_cuboid(fl[0], iaxes, vlab, idomains)
self.cuboid.set_orientation(an)
else:
self.cuboid = None
def test_tree_to_cuboid(self):
from pyhrf.ndarray import xndarray, tree_to_cuboid
import numpy as _np
d1 = {}
set_leaf(d1, ['1','2.1','3.1'], xndarray(_np.array([1])))
set_leaf(d1, ['1','2.1','3.2'], xndarray(_np.array([2])))
set_leaf(d1, ['1','2.2','3.3'], xndarray(_np.array([3])))
d2 = {}
set_leaf(d2, ['1','2.1','3.1'], xndarray(_np.array([10])))
set_leaf(d2, ['1','2.1','3.2'], xndarray(_np.array([11])))
set_leaf(d2, ['1','2.2','3.3'], xndarray(_np.array([12])))
d = {'d1':d1, 'd2':d2}
labels = ['case', 'p1', 'p2', 'p3']
c = tree_to_cuboid(d, labels)
def analyse_roi(self, fdata):
pyhrf.verbose(1, 'Run GLM analysis (ROI %d) ...' %fdata.get_roi_id())
if self.rescale_factor is not None:
m = np.where(fdata.roiMask)
rescale_factor = self.rescale_factor[:,m[0],m[1],m[2]]
else:
rescale_factor = None
glm, dm, cons = glm_nipy(fdata, contrasts=self.contrasts,
hrf_model=self.hrf_model,
drift_model=self.drift_model,
hfcut=self.hfcut,
residuals_model=self.residuals_model,
fit_method=self.fit_method,
fir_delays=self.fir_delays,
rescale_results=self.rescale_results,
rescale_factor=rescale_factor)
outputs = {}
ns, nr = dm.matrix.shape
tr = fdata.tr
if rescale_factor is not None:
#same sf for all voxels
dm.matrix[:,:rescale_factor.shape[0]] /= rescale_factor[:,0]
cdesign_matrix = xndarray(dm.matrix,
axes_names=['time','regressor'],
axes_domains={'time':np.arange(ns)*tr,
'regressor':dm.names})
outputs['design_matrix'] = cdesign_matrix
axes_names = ['time', 'voxel']
axes_domains = {'time' : np.arange(ns)*tr}
bold = xndarray(fdata.bold.astype(np.float32),
axes_names=axes_names,
axes_domains=axes_domains,
value_label='BOLD')
fit = np.dot(dm.matrix, glm.beta)
cfit = xndarray(fit, axes_names=['time','voxel'],
axes_domains={'time':np.arange(ns)*tr})
outputs['bold_fit'] = stack_cuboids([bold,cfit], 'stype', ['bold', 'fit'])
nb_cond = fdata.nbConditions
fit_cond = np.dot(dm.matrix[:,:nb_cond], glm.beta[:nb_cond,:])
fit_cond -= fit_cond.mean(0)
fit_cond += fdata.bold.mean(0)
outputs['fit_cond'] = xndarray(fit_cond, axes_names=['time','voxel'],
axes_domains={'time':np.arange(ns)*tr})
outputs['s2'] = xndarray(glm.s2, axes_names=['voxel'])
if 0:
cbeta = xndarray(glm.beta, axes_names=['reg_name','voxel'],
axes_domains={'reg_name':dm.names})
outputs['beta'] = cbeta
else:
if self.hrf_model == 'FIR':
fir = dict((d * fdata.tr, OrderedDict()) for d in self.fir_delays)
for ib, bname in enumerate(dm.names):
outputs['beta_' + bname] = xndarray(glm.beta[ib],
axes_names=['voxel'])
if self.hrf_model == 'FIR' and 'delay' in bname:
#reconstruct filter:
cond, delay = bname.split('_delay_')
delay = int(delay) * fdata.tr
fir[delay][cond] = xndarray(glm.beta[ib], axes_names=['voxel'])
if self.hrf_model == 'FIR':
chrf = tree_to_cuboid(fir, ['time', 'condition'])
outputs['hrf'] = chrf
outputs['hrf_norm'] = (chrf**2).sum('time')**.5
for cname, con in cons.iteritems():
#print 'con:'
#print dir(con)
outputs['con_effect_'+cname] = xndarray(con.effect,
axes_names=['voxel'])
#print '%%%%%%% con.variance:', con.variance.shape
ncon = con.effect / con.variance.std()
outputs['ncon_effect_'+cname] = xndarray(ncon, axes_names=['voxel'])
outputs['con_pvalue_'+cname] = xndarray(con.pvalue(self.con_bl),
axes_names=['voxel'])
roi_lab_vol = np.zeros(fdata.get_nb_vox_in_mask(), dtype=np.int32) + \
fdata.get_roi_id()
outputs['mask'] = xndarray(roi_lab_vol, axes_names=['voxel'])
# for ib, bname in enumerate(design_matrix.names):
# beta_vol = expand_array_in_mask(my_glm.beta[ib], mask_array)
# beta_image = Nifti1Image(beta_vol, affine)
# beta_file = op.join(output_dir, 'beta_%s.nii' %bname)
# save(beta_image, beta_file)
# beta_files.append(beta_file)
return outputs
def __init__(self, parameters=None, xmlHandler=NumpyXMLHandler(), xmlLabel=None, xmlComment=None):
if 0:
print "xndarrayXml2.__init__ ..."
XMLParamDrivenClass.__init__(self, parameters, xmlHandler, xmlLabel, xmlComment)
clabel = self.parameters["name"]
ad = self.parameters["domains"]
an = self.parameters["orientation"]
vlab = self.parameters["value_label"]
fl = self.parameters["dataFiles"]
eaxes = self.parameters["explodedAxes"]
self.outDir = "./"
self.meta_data = None
# print ''
# print 'clabel:', clabel
# print 'eaxes :', eaxes
# print 'an:', an
if len(fl) > 0 and self.loadxndarray:
ed = dict(zip(eaxes, [ad[ea] for ea in eaxes]))
if len(eaxes) > 0:
iaxes = []
for a in an:
if a not in eaxes:
iaxes.append(a)
# iaxes = list(set(an).difference(eaxes))
else:
iaxes = an
idomains = dict(zip(iaxes, [ad[ia] for ia in iaxes]))
ctree = {}
if 0:
print "fl:", fl
print "id:", idomains
print "iaxes:", iaxes
print "ad:", ad
print "ed:", ed
print "eaxes:", eaxes
if len(eaxes) > 0:
for slice, fn in fl.iteritems():
slice = list(slice)
if 0:
print "slice:", slice
print "fn:", fn
if slice is not None:
if "_" in slice:
slice.remove("_")
if "_" in slice:
slice.remove("_")
cdata = self.load_cuboid(fn, iaxes, vlab, idomains)
# print 'ctree:'
# print ctree
# print 'cdata:', cdata
set_leaf(ctree, list(slice), cdata)
self.cuboid = tree_to_cuboid(ctree, branchLabels=eaxes)
else:
self.cuboid = self.load_cuboid(fl.values()[0], iaxes, vlab, idomains)
self.cuboid.set_orientation(an)
else:
self.cuboid = None
self.useRelativePath = False
