def check_final_value(self):
report = {}
if self.samplerEngine.check_ftval is not None:
if self.trueValue is None:
pyhrf.verbose(4, "Warning: no true val to check against for %s" % self.name)
elif self.sampleFlag:
fv = self.get_final_value()
tv = self.get_true_value()
rtol = 0.1
atol = 0.1
# report['true_value'] = tv
# report['final_value'] = fv
abs_error = np.abs(tv - fv)
report["abs_error"] = abs_error
rel_error = abs_error / np.maximum(np.abs(tv), np.abs(fv))
report["rel_error"] = rel_error
report["accuracy"] = self.get_accuracy(abs_error, rel_error, fv, tv, atol, rtol)
is_accurate = report["accuracy"][1].all()
pyhrf.verbose(
2,
"Fit error for %s: aerr=%f, rerr=%f, "
"is_accurate=%s" % (self.name, abs_error.mean(), rel_error.mean(), is_accurate),
)
if not is_accurate:
m = (
"Final value of %s is not close to "
"true value.\n -> aerror: %s\n -> rerror: %s\n"
" Final value:\n %s\n True value:\n %s\n"
% (
self.name,
array_summary(report["abs_error"]),
array_summary(report["rel_error"]),
str(fv),
str(tv),
)
)
if self.samplerEngine.check_ftval == "raise":
raise Exception(m)
elif self.samplerEngine.check_ftval == "print":
print "\n".join(["!! " + s for s in m.split("\n")])
self.report_check_ft_val = report
def check_against_truth(self, atol, rtol, inaccuracy_handling='print'):
fv = self.get_estim_value_for_check()
tv = self.get_true_value_for_check()
if tv is None:
pyhrf.verbose(4, 'Warning: no true val to check against for %s' \
%self.name)
elif self.do_sampling: #when sampling is off, assume there is no need
# to check against truth
abs_error = np.abs(tv - fv)
rel_error = abs_error/np.maximum(np.abs(tv),np.abs(fv))
acc = self.get_accuracy_against_truth(abs_error, rel_error, fv, tv,
atol, rtol)
is_accurate = acc[1].all()
pyhrf.verbose(3, 'Check error for "%s" -> estim: %s, '\
'truth: %s, atol=%f, rtol=%f'
%(self.name, str(fv), str(tv), atol, rtol))
pyhrf.verbose(2, 'Fit error for "%s": avg aerr=%f, avg rerr=%f, '\
'is_accurate=%s' %(self.name, abs_error.mean(),
rel_error.mean(),
is_accurate))
if not is_accurate:
m = "Final value of %s is not close to " \
"true value.\n -> aerror: %s\n -> rerror: %s\n" \
" Final value:\n %s\n True value:\n %s\n" \
%(self.name, array_summary(abs_error),
array_summary(rel_error), str(fv), str(tv))
if inaccuracy_handling == 'raise':
raise Exception(m)
elif inaccuracy_handling == 'print':
print '\n'.join(['!! '+ s for s in m.split('\n')])
self.truth_checking_report = {
'abs_error' : abs_error,
'rel_error' : rel_error}
def check_final_value(self):
report = {}
if self.samplerEngine.check_ftval is not None:
if self.trueValue is None:
logger.info('Warning: no true val to check against for %s',
self.name)
elif self.sampleFlag:
fv = self.get_final_value()
tv = self.get_true_value()
rtol = 0.1 # Relative tolerance value: 10%
# Absolute tolerance value. TODO: dependency to parameter
atol = 0.1
# report['true_value'] = tv
# report['final_value'] = fv
abs_error = np.abs(tv - fv)
report['abs_error'] = abs_error
rel_error = abs_error / np.maximum(np.abs(tv), np.abs(fv))
report['rel_error'] = rel_error
report['accuracy'] = self.get_accuracy(abs_error, rel_error,
fv, tv, atol, rtol)
is_accurate = report['accuracy'][1].all()
logger.info('Fit error for %s: aerr=%f, rerr=%f, is_accurate=%s',
self.name, abs_error.mean(), rel_error.mean(),
is_accurate)
if not is_accurate:
m = "Final value of %s is not close to " \
"true value.\n -> aerror: %s\n -> rerror: %s\n" \
" Final value:\n %s\n True value:\n %s\n" \
% (self.name, array_summary(report['abs_error']),
array_summary(report['rel_error']),
str(fv), str(tv))
if self.samplerEngine.check_ftval == 'raise':
raise Exception(m)
elif self.samplerEngine.check_ftval == 'print':
print '\n'.join(['!! ' + s for s in m.split('\n')])
self.report_check_ft_val = report
def check_final_value(self):
report = {}
if self.samplerEngine.check_ftval is not None:
if self.trueValue is None:
logger.info('Warning: no true val to check against for %s',
self.name)
elif self.sampleFlag:
fv = self.get_final_value()
tv = self.get_true_value()
rtol = 0.1 # Relative tolerance value: 10%
# Absolute tolerance value. TODO: dependency to parameter
atol = 0.1
# report['true_value'] = tv
# report['final_value'] = fv
abs_error = np.abs(tv - fv)
report['abs_error'] = abs_error
rel_error = abs_error / np.maximum(np.abs(tv), np.abs(fv))
report['rel_error'] = rel_error
report['accuracy'] = self.get_accuracy(abs_error, rel_error,
fv, tv, atol, rtol)
is_accurate = report['accuracy'][1].all()
logger.info(
'Fit error for %s: aerr=%f, rerr=%f, is_accurate=%s',
self.name, abs_error.mean(), rel_error.mean(), is_accurate)
if not is_accurate:
m = "Final value of %s is not close to " \
"true value.\n -> aerror: %s\n -> rerror: %s\n" \
" Final value:\n %s\n True value:\n %s\n" \
% (self.name, array_summary(report['abs_error']),
array_summary(report['rel_error']),
str(fv), str(tv))
if self.samplerEngine.check_ftval == 'raise':
raise Exception(m)
elif self.samplerEngine.check_ftval == 'print':
print '\n'.join(['!! ' + s for s in m.split('\n')])
self.report_check_ft_val = report
def check_against_truth(self, atol, rtol, inaccuracy_handling='print'):
fv = self.get_estim_value_for_check()
tv = self.get_true_value_for_check()
if tv is None:
logger.info(
'Warning: no true val to check against for %s', self.name)
elif self.do_sampling: # when sampling is off, assume there is no need
# to check against truth
abs_error = np.abs(tv - fv)
rel_error = abs_error / np.maximum(np.abs(tv), np.abs(fv))
acc = self.get_accuracy_against_truth(abs_error, rel_error, fv, tv,
atol, rtol)
is_accurate = acc[1].all()
logger.info('Check error for "%s" -> '
'estim: %s, truth: %s, atol=%f, rtol=%f', self.name,
str(fv), str(tv), atol, rtol)
logger.info('Fit error for "%s": avg aerr=%f, avg rerr=%f, '
'is_accurate=%s', self.name, abs_error.mean(),
rel_error.mean(), is_accurate)
if not is_accurate:
m = "Final value of %s is not close to " \
"true value.\n -> aerror: %s\n -> rerror: %s\n" \
" Final value:\n %s\n True value:\n %s\n" \
% (self.name, array_summary(abs_error),
array_summary(rel_error), str(fv), str(tv))
if inaccuracy_handling == 'raise':
raise Exception(m)
elif inaccuracy_handling == 'print':
print '\n'.join(['!! ' + s for s in m.split('\n')])
self.truth_checking_report = {
'abs_error': abs_error,
'rel_error': rel_error}
def get_string_value(self, v):
if v.size == 1:
return "%1.4f" % v[0]
if self.axes_names is not None and "condition" == self.axes_names[0]:
cNames = self.dataInput.cNames
if v.ndim == 1:
return get_2Dtable_string(v, cNames, ["Value"])
else:
# print 'cNames:', cNames
# print 'v.shape:', v.shape
v = v[: len(cNames), :]
return get_2Dtable_string(v.reshape(v.shape[0], 1, -1), cNames, ["Value"])
else:
return array_summary(v)
def run(self):
"""
function to launch the analysis
"""
logger.info('Starting voxel-wise HRF estimation ...')
logger.info('nvox=%d, ncond=%d, nscans=%d', self.nbVoxels, self.M,
self.ImagesNb)
# initialization of the matrices that will store all voxel resuls
logger.info("Init storage ...")
self.InitStorageMat()
logger.info("Compute onset matrix ...")
self.Compute_onset_matrix3()
self.stop_iterations = np.zeros(self.bold.shape[1], dtype=int)
# voxelwise analysis. This loop handles the currently analyzed voxels.
for POI in xrange(self.bold.shape[1]): # POI = point of interest
t0 = time()
logger.info("Point %s / %s", str(POI), str(self.nbVoxels))
self.ReadPointOfInterestData(POI)
# initialize with zeros or ones all matrix and vector used in
# the class
logger.info("Init matrix and vectors ...")
self.InitMatrixAndVectors(POI)
# compute onset matrix
# compute low frequency basis
logger.info('build low freq mat ...')
self.buildLowFreqMat()
# precompute usefull data
logger.info('Compute inv R ...')
self.Compute_INV_R_and_R_and_DET_R()
# solve find response functions and hyperparameters
logger.info('EM solver ...')
self.EM_solver(POI)
# store current results in matrices initialized in 'InitStoringMat'
logger.info('Store res ...')
self.StoreRes(POI)
logger.info("Done in %s", format_duration(time() - t0))
self.clean_memory()
logger.info('Nb of iterations to reach stop crit: %s',
array_summary(self.stop_iterations))
def run(self):
"""
function to launch the analysis
"""
logger.info('Starting voxel-wise HRF estimation ...')
logger.info('nvox=%d, ncond=%d, nscans=%d', self.nbVoxels, self.M,
self.ImagesNb)
# initialization of the matrices that will store all voxel resuls
logger.info("Init storage ...")
self.InitStorageMat()
logger.info("Compute onset matrix ...")
self.Compute_onset_matrix3()
self.stop_iterations = np.zeros(self.bold.shape[1], dtype=int)
# voxelwise analysis. This loop handles the currently analyzed voxels.
for POI in xrange(self.bold.shape[1]): # POI = point of interest
t0 = time()
logger.info("Point %s / %s", str(POI), str(self.nbVoxels))
self.ReadPointOfInterestData(POI)
# initialize with zeros or ones all matrix and vector used in
# the class
logger.info("Init matrix and vectors ...")
self.InitMatrixAndVectors(POI)
# compute onset matrix
# compute low frequency basis
logger.info('build low freq mat ...')
self.buildLowFreqMat()
# precompute usefull data
logger.info('Compute inv R ...')
self.Compute_INV_R_and_R_and_DET_R()
# solve find response functions and hyperparameters
logger.info('EM solver ...')
self.EM_solver(POI)
# store current results in matrices initialized in 'InitStoringMat'
logger.info('Store res ...')
self.StoreRes(POI)
logger.info("Done in %s", format_duration(time() - t0))
self.clean_memory()
logger.info('Nb of iterations to reach stop crit: %s',
array_summary(self.stop_iterations))
def get_string_value(self, v):
if v.size == 1:
return '%1.4f' % v[0]
if self.axes_names is not None and 'condition' == self.axes_names[0]:
cNames = self.dataInput.cNames
if v.ndim == 1:
return get_2Dtable_string(v, cNames, ['Value'])
else:
# print 'cNames:', cNames
# print 'v.shape:', v.shape
v = v[:len(cNames), :]
return get_2Dtable_string(v.reshape(v.shape[0], 1, -1), cNames,
['Value'])
else:
return array_summary(v)
def run(self):
"""
function to launch the analysis
"""
pyhrf.verbose(1, 'Starting voxel-wise HRF estimation ...')
pyhrf.verbose(1, 'nvox=%d, ncond=%d, nscans=%d' \
%(self.nbVoxels,self.M,self.ImagesNb))
#initialization of the matrices that will store all voxel resuls
pyhrf.verbose(3,"Init storage ...")
self.InitStorageMat()
pyhrf.verbose(3,"Compute onset matrix ...")
self.Compute_onset_matrix3()
self.stop_iterations = np.zeros(self.bold.shape[1], dtype=int)
#voxelwise analysis. This loop handles the currently analyzed voxels.
for POI in xrange(self.bold.shape[1]): # POI = point of interest
t0 = time()
pyhrf.verbose(3,"Point "+str(POI)+" / "+str(self.nbVoxels))
self.ReadPointOfInterestData(POI)
#print "Signal in point " , POI , "read"
# if hasattr(self, 'X'):
# prevX = copyModule.deepcopy(self.X)
# else:
# prevX = None
#initialize with zeros or ones all matrix and vector used in
#the class
pyhrf.verbose(4, "Init matrix and vectors ...")
self.InitMatrixAndVectors(POI)
#print "Matrix and vectors initialized"
#compute onset matrix
# print "Onset matrix computed"
# if prevX is not None:
# for i,x in enumerate(self.X):
# print 'sess %d ->' %i, (prevX[i]!=x).any()
# if 0:
# for m in xrange(prevX[i].shape[0]):
# for n in xrange(prevX[i].shape[1]):
# for k in xrange(prevX[i].shape[2]):
# print int(prevX[i][m,n,k]),'',
# print ''
# print ''
# print ''
# for m in xrange(x.shape[0]):
# for n in xrange(x.shape[1]):
# for k in xrange(x.shape[2]):
# print int(x[m,n,k]),
# print ''
# print ''
# print ''
#compute low frequency basis
pyhrf.verbose(4, 'build low freq mat ...')
self.buildLowFreqMat()
#print "Low frequency matrix generated"
#precompute usefull data
pyhrf.verbose(4, 'Compute inv R ...')
self.Compute_INV_R_and_R_and_DET_R()
#print "R^{-1} and det(R) precomputed"
#solve find response functions and hyperparameters
pyhrf.verbose(4, 'EM solver ...')
self.EM_solver(POI)
#store current results in matrices initialized in 'InitStoringMat'
pyhrf.verbose(4, 'Store res ...')
self.StoreRes(POI)
pyhrf.verbose(3,"Done in %s" %format_duration(time() - t0) )
self.clean_memory()
pyhrf.verbose(1, 'Nb of iterations to reach stop crit: %s' \
%array_summary(self.stop_iterations))
