def marshall(self): yts=md.constants.yts; WriteData(fid,prefix,'object',self,'class','inversion','fieldname','iscontrol','format','Boolean'); WriteData(fid,prefix,'name','md.inversion.type','data',4,'format','Integer'); if not self.iscontrol: return WriteData(fid,prefix,'object',self,'class','inversion','fieldname','control_scaling_factors','format','DoubleMat','mattype',3); WriteData(fid,prefix,'object',self,'class','inversion','fieldname','maxsteps','format','Integer'); WriteData(fid,prefix,'object',self,'class','inversion','fieldname','maxiter','format','Integer'); WriteData(fid,prefix,'object',self,'class','inversion','fieldname','dxmin','format','Double'); WriteData(fid,prefix,'object',self,'class','inversion','fieldname','gttol','format','Double'); WriteData(fid,prefix,'object',self,'class','inversion','fieldname','cost_functions_coefficients','format','DoubleMat','mattype',1); WriteData(fid,prefix,'object',self,'class','inversion','fieldname','min_parameters','format','DoubleMat','mattype',3); WriteData(fid,prefix,'object',self,'class','inversion','fieldname','max_parameters','format','DoubleMat','mattype',3); WriteData(fid,prefix,'object',self,'class','inversion','fieldname','vx_obs','format','DoubleMat','mattype',1,'scale',1./yts); WriteData(fid,prefix,'object',self,'class','inversion','fieldname','vy_obs','format','DoubleMat','mattype',1,'scale',1./yts); WriteData(fid,prefix,'object',self,'class','inversion','fieldname','vz_obs','format','DoubleMat','mattype',1,'scale',1./yts); if(numel(self.thickness_obs)==md.mesh.numberofelements): mattype=2; else: mattype=1; WriteData(fid,prefix,'object',self,'class','inversion','fieldname','thickness_obs','format','DoubleMat','mattype',mattype); WriteData(fid,prefix,'object',self,'class','inversion','fieldname','surface_obs','format','DoubleMat','mattype',mattype); #process control parameters num_control_parameters = np.numel(self.control_parameters); WriteData(fid,prefix,'object',self,'fieldname','control_parameters','format','StringArray'); WriteData(fid,prefix,'data',num_control_parameters,'name','md.inversion.num_control_parameters','format','Integer'); #process cost functions num_cost_functions=np.size(self.cost_functions,2); data=copy.deepcopy(self.cost_functions) data[np.nonzero(self.cost_functions==101)] =['SurfaceAbsVelMisfit']; data[np.nonzero(self.cost_functions==102)]=['SurfaceRelVelMisfit']; data[np.nonzero(self.cost_functions==103)]=['SurfaceLogVelMisfit']; data[np.nonzero(self.cost_functions==104)]=['SurfaceLogVxVyMisfit']; data[np.nonzero(self.cost_functions==105)]=['SurfaceAverageVelMisfit']; data[np.nonzero(self.cost_functions==201)]=['ThicknessAbsMisfit']; data[np.nonzero(self.cost_functions==501)]=['DragCoefficientAbsGradient']; data[np.nonzero(self.cost_functions==502)]=['RheologyBbarAbsGradient']; data[np.nonzero(self.cost_functions==503)]=['ThicknessAbsGradient']; data[np.nonzero(self.cost_functions==504)]=['ThicknessAlongGradient']; data[np.nonzero(self.cost_functions==505)]=['ThicknessAcrossGradient']; data[np.nonzero(self.cost_functions==506)]=['BalancethicknessMisfit']; data[np.nonzero(self.cost_functions==601)]=['SurfaceAbsMisfit']; data[np.nonzero(self.cost_functions==1001)]=['Outputdefinition1']; data[np.nonzero(self.cost_functions==1002)]=['Outputdefinition2']; data[np.nonzero(self.cost_functions==1003)]=['Outputdefinition3']; data[np.nonzero(self.cost_functions==1004)]=['Outputdefinition4']; data[np.nonzero(self.cost_functions==1005)]=['Outputdefinition5']; data[np.nonzero(self.cost_functions==1006)]=['Outputdefinition6']; data[np.nonzero(self.cost_functions==1007)]=['Outputdefinition7']; data[np.nonzero(self.cost_functions==1008)]=['Outputdefinition8']; data[np.nonzero(self.cost_functions==1009)]=['Outputdefinition8']; data[np.nonzero(self.cost_functions==1010)]=['Outputdefinition10']; WriteData(fid,prefix,'data',data,'name','md.inversion.cost_functions','format','StringArray'); WriteData(fid,prefix,'data',num_cost_functions,'name','md.inversion.num_cost_functions','format','Integer');
def checkconsistency(self, md, solution, analyses):
# Early return
if not self.iscontrol:
return
if not IssmConfig('_HAVE_M1QN3_')[0]:
md = checkmessage(md,['M1QN3 has not been installed, ISSM needs to be reconfigured and recompiled with AD'])
num_controls=np.numel(md.inversion.control_parameters)
num_costfunc=np.size(md.inversion.cost_functions,2)
md = checkfield(md,'fieldname','inversion.iscontrol','values',[0, 1])
md = checkfield(md,'fieldname','inversion.control_parameters','cell',1,'values',\
['BalancethicknessThickeningRate' 'FrictionCoefficient' 'MaterialsRheologyBbar' 'DamageDbar',\
'Vx' 'Vy' 'Thickness' 'BalancethicknessOmega' 'BalancethicknessApparentMassbalance'])
md = checkfield(md,'fieldname','inversion.control_scaling_factors','size',[1, num_controls],'>',0,float('Nan'),1)
md = checkfield(md,'fieldname','inversion.maxsteps','numel',1,'>=',0)
md = checkfield(md,'fieldname','inversion.maxiter','numel',1,'>=',0)
md = checkfield(md,'fieldname','inversion.dxmin','numel',1,'>',0)
md = checkfield(md,'fieldname','inversion.gttol','numel',1,'>',0)
md = checkfield(md,'fieldname','inversion.cost_functions','size',[1, num_costfunc],'values', [i for i in range(101,106)]+[201]+[i for i in range(501,507)]+[i for i in range(601,605)]+[i for i in range(1001, 1011)])
md = checkfield(md,'fieldname','inversion.cost_functions_coefficients','size',[md.mesh.numberofvertices, num_costfunc],'>=',0)
md = checkfield(md,'fieldname','inversion.min_parameters','size',[md.mesh.numberofvertices, num_controls])
md = checkfield(md,'fieldname','inversion.max_parameters','size',[md.mesh.numberofvertices, num_controls])
if solution=='BalancethicknessSolution':
md = checkfield(md,'fieldname','inversion.thickness_obs','size',[md.mesh.numberofvertices],float('Nan'),1)
md = checkfield(md,'fieldname','inversion.surface_obs','size',[md.mesh.numberofvertices], float('Nan'),1)
elif solution=='BalancethicknessSoftSolution':
md = checkfield(md,'fieldname','inversion.thickness_obs','size',[md.mesh.numberofvertices],float('Nan'),1)
else:
md = checkfield(md,'fieldname','inversion.vx_obs','size',[md.mesh.numberofvertices],float('Nan'),1)
if not np.strcmp(domaintype(md.mesh),'2Dvertical'):
md = checkfield(md,'fieldname','inversion.vy_obs','size',[md.mesh.numberofvertices],float('Nan'),1)
return md
def marshall(self, md, fid): yts=365.0*24.0*3600.0; WriteData(fid,'object',self,'class','inversion','fieldname','iscontrol','format','Boolean') WriteData(fid,'enum',InversionTypeEnum(),'data',1,'format','Integer') if not self.iscontrol: return WriteData(fid,'object',self,'class','inversion','fieldname','incomplete_adjoint','format','Boolean') WriteData(fid,'object',self,'class','inversion','fieldname','maxsteps','format','Integer') WriteData(fid,'object',self,'class','inversion','fieldname','maxiter','format','Integer') WriteData(fid,'object',self,'class','inversion','fieldname','fatol','format','Double') WriteData(fid,'object',self,'class','inversion','fieldname','frtol','format','Double') WriteData(fid,'object',self,'class','inversion','fieldname','gatol','format','Double') WriteData(fid,'object',self,'class','inversion','fieldname','grtol','format','Double') WriteData(fid,'object',self,'class','inversion','fieldname','gttol','format','Double') WriteData(fid,'object',self,'class','inversion','fieldname','algorithm','format','String') WriteData(fid,'object',self,'class','inversion','fieldname','cost_functions_coefficients','format','DoubleMat','mattype',1) WriteData(fid,'object',self,'class','inversion','fieldname','min_parameters','format','DoubleMat','mattype',3) WriteData(fid,'object',self,'class','inversion','fieldname','max_parameters','format','DoubleMat','mattype',3) WriteData(fid,'object',self,'class','inversion','fieldname','vx_obs','format','DoubleMat','mattype',1,'scale',1./yts) WriteData(fid,'object',self,'class','inversion','fieldname','vy_obs','format','DoubleMat','mattype',1,'scale',1./yts) WriteData(fid,'object',self,'class','inversion','fieldname','vz_obs','format','DoubleMat','mattype',1,'scale',1./yts) WriteData(fid,'object',self,'class','inversion','fieldname','thickness_obs','format','DoubleMat','mattype',1) WriteData(fid,'object',self,'class','inversion','fieldname','surface_obs','format','DoubleMat','mattype',1) #process control parameters num_control_parameters = numpy.numel(self.control_parameters) data = numpy.array([StringToEnum(self.control_parameter[0]) for control_parameter in self.control_parameters]).reshape(1,-1) WriteData(fid,'data',data,'enum',InversionControlParametersEnum(),'format','DoubleMat','mattype',3) WriteData(fid,'data',num_control_parameters,'enum',InversionNumControlParametersEnum(),'format','Integer') #process cost functions num_cost_functions = numpy.size(self.cost_functions,2) data= marshallcostfunctions(self.cost_functions) WriteData(fid,'data',data,'enum',InversionCostFunctionsEnum(),'format','DoubleMat','mattype',3) WriteData(fid,'data',num_cost_functions,'enum',InversionNumCostFunctionsEnum(),'format','Integer')
def checkconsistency(self,md,solution,analyses):
if not self.control:
return md
if not IssmConfig('_HAVE_TAO_'):
md = checkmessage(md,['TAO has not been installed, ISSM needs to be reconfigured and recompiled with TAO'])
num_controls= numpy.numel(md.inversion.control_parameters)
num_costfunc= numpy.size(md.inversion.cost_functions,2)
md = checkfield(md,'fieldname','inversion.iscontrol','values',[0, 1])
md = checkfield(md,'fieldname','inversion.incomplete_adjoint','values',[0, 1])
md = checkfield(md,'fieldname','inversion.control_parameters','cell',1,'values',supportedcontrols())
md = checkfield(md,'fieldname','inversion.maxsteps','numel',1,'>=',0)
md = checkfield(md,'fieldname','inversion.maxiter','numel',1,'>=',0)
md = checkfield(md,'fieldname','inversion.fatol','numel',1,'>=',0)
md = checkfield(md,'fieldname','inversion.frtol','numel',1,'>=',0)
md = checkfield(md,'fieldname','inversion.gatol','numel',1,'>=',0)
md = checkfield(md,'fieldname','inversion.grtol','numel',1,'>=',0)
md = checkfield(md,'fieldname','inversion.gttol','numel',1,'>=',0)
PETSCMAJOR = IssmConfig('_PETSC_MAJOR_')
PETSCMINOR = IssmConfig('_PETSC_MINOR_')
if(PETSCMAJOR>3 or (PETSCMAJOR==3 and PETSCMINOR>=5)):
md = checkfield(md,'fieldname','inversion.algorithm','values',{'blmvm','cg','lmvm'})
else:
md = checkfield(md,'fieldname','inversion.algorithm','values',{'tao_blmvm','tao_cg','tao_lmvm'})
md = checkfield(md,'fieldname','inversion.cost_functions','size',[1, num_costfunc],'values',supportedcostfunctions())
md = checkfield(md,'fieldname','inversion.cost_functions_coefficients','size',[md.mesh.numberofvertices, num_costfunc],'>=',0)
md = checkfield(md,'fieldname','inversion.min_parameters','size',[md.mesh.numberofvertices, num_controls])
md = checkfield(md,'fieldname','inversion.max_parameters','size',[md.mesh.numberofvertices, num_controls])
if solution==BalancethicknessSolutionEnum():
md = checkfield(md,'fieldname','inversion.thickness_obs','size',[md.mesh.numberofvertices, 1],'NaN',1,'Inf',1)
elif solution==BalancethicknessSoftSolutionEnum():
md = checkfield(md,'fieldname','inversion.thickness_obs','size',[md.mesh.numberofvertices, 1],'NaN',1,'Inf',1)
else:
md = checkfield(md,'fieldname','inversion.vx_obs','size',[md.mesh.numberofvertices, 1],'NaN',1,'Inf',1)
md = checkfield(md,'fieldname','inversion.vy_obs','size',[md.mesh.numberofvertices, 1],'NaN',1,'Inf',1)
def marshall(self, md, fid):
yts=365.0*24.0*3600.0;
WriteData(fid,'object',self,'class','inversion','fieldname','iscontrol','format','Boolean')
WriteData(fid,'enum',InversionTypeEnum(),'data',1,'format','Integer')
if not self.iscontrol:
return
WriteData(fid,'object',self,'class','inversion','fieldname','incomplete_adjoint','format','Boolean')
WriteData(fid,'object',self,'class','inversion','fieldname','maxsteps','format','Integer')
WriteData(fid,'object',self,'class','inversion','fieldname','maxiter','format','Integer')
WriteData(fid,'object',self,'class','inversion','fieldname','fatol','format','Double')
WriteData(fid,'object',self,'class','inversion','fieldname','frtol','format','Double')
WriteData(fid,'object',self,'class','inversion','fieldname','gatol','format','Double')
WriteData(fid,'object',self,'class','inversion','fieldname','grtol','format','Double')
WriteData(fid,'object',self,'class','inversion','fieldname','gttol','format','Double')
WriteData(fid,'object',self,'class','inversion','fieldname','algorithm','format','String')
WriteData(fid,'object',self,'class','inversion','fieldname','cost_functions_coefficients','format','DoubleMat','mattype',1)
WriteData(fid,'object',self,'class','inversion','fieldname','min_parameters','format','DoubleMat','mattype',3)
WriteData(fid,'object',self,'class','inversion','fieldname','max_parameters','format','DoubleMat','mattype',3)
WriteData(fid,'object',self,'class','inversion','fieldname','vx_obs','format','DoubleMat','mattype',1,'scale',1./yts)
WriteData(fid,'object',self,'class','inversion','fieldname','vy_obs','format','DoubleMat','mattype',1,'scale',1./yts)
WriteData(fid,'object',self,'class','inversion','fieldname','vz_obs','format','DoubleMat','mattype',1,'scale',1./yts)
WriteData(fid,'object',self,'class','inversion','fieldname','thickness_obs','format','DoubleMat','mattype',1)
WriteData(fid,'object',self,'class','inversion','fieldname','surface_obs','format','DoubleMat','mattype',1)
#process control parameters
num_control_parameters = numpy.numel(self.control_parameters)
data = numpy.array([StringToEnum(self.control_parameter[0]) for control_parameter in self.control_parameters]).reshape(1,-1)
WriteData(fid,'data',data,'enum',InversionControlParametersEnum(),'format','DoubleMat','mattype',3)
WriteData(fid,'data',num_control_parameters,'enum',InversionNumControlParametersEnum(),'format','Integer')
#process cost functions
num_cost_functions = numpy.size(self.cost_functions,2)
data= marshallcostfunctions(self.cost_functions)
WriteData(fid,'data',data,'enum',InversionCostFunctionsEnum(),'format','DoubleMat','mattype',3)
WriteData(fid,'data',num_cost_functions,'enum',InversionNumCostFunctionsEnum(),'format','Integer')
def mri_window(image_in: np.ndarray,
mask: Optional[np.ndarray],
output_range: Tuple[float, float] = (-1.0, 1.0),
sharpen: float = 1.9,
tail: Union[List[float], float] = 1.0,
debug_mode: bool = False) -> Tuple[np.array, str]:
"""
This function takes an MRI Image, removes to first peak of values (air). Then a window range is found centered
around the mean of the remaining values and with a range controlled by the standard deviation and the sharpen
input parameter. The larger sharpen is, the wider the range. The resulting values are the normalised to the given
output_range, with values below and above the range being set the the boundary values.
:param image_in: The image to normalize.
:param mask: Consider only pixel values of the input image for which the mask is non-zero. If None the whole
image is considered.
:param output_range: The desired value range of the result image.
:param sharpen: number of standard deviation either side of mean to include in the window
:param tail: Default 1, allow window range to include more of tail of distribution.
:param debug_mode: If true, create diagnostic plots.
:return: normalized image
"""
nchannel = image_in.shape[0]
imout = np.zeros_like(image_in)
if isinstance(tail, int):
tail = float(tail)
if isinstance(tail, float):
tail = [tail] * nchannel
status = ""
for ichannel in range(nchannel):
if ichannel > 0:
status += "Channel {}: ".format(ichannel)
# Flatten to apply Otsu_thresholding
imflat = image_in[ichannel, ...].flatten()
if mask is None:
maflat = None
in_mask = False
else:
maflat = mask.flatten()
in_mask = mask > 0
# Find Otsu's threshold for the values of the input image
threshold = threshold_otsu(imflat)
# Find window level
level, std_i, _, max_foreground = robust_mean_std(
imflat[imflat > threshold])
# If lower value of window is below threshold replace lower value with threshold
input_range = (max(level - std_i * sharpen, threshold),
min(max_foreground,
level + tail[ichannel] * std_i * sharpen))
im_thresh = image_in[ichannel, ...]
im_thresh[image_in[ichannel, ...] < threshold] = 0
# Use Polynomial transform function to convert data to output range
imout[ichannel,
...] = LinearTransform.transform(im_thresh, input_range,
output_range)
status += f"Otsu {threshold:0.0f}, level {level:0.0f}, range ({input_range[0]:0.0f}, {input_range[1]:0.0f}) "
logging.debug(status)
if debug_mode:
print('Otsu {}, range {}'.format(threshold, input_range))
if mask is None:
no_thresh = np.sum(imflat < threshold)
no_high = np.sum(imout == output_range[1])
pc_thresh = no_thresh / np.numel(imflat) * 100
pc_high = no_high / np.numel(imflat) * 100
else:
no_thresh = np.sum(imflat[maflat == 1] < threshold)
no_high = np.sum(imout == output_range[1])
pc_thresh = no_thresh / np.sum(in_mask) * 100
pc_high = no_high / np.sum(in_mask) * 100
print('Percent of values outside window range: low,high',
pc_thresh, pc_high, no_high)
with open("channels_trim.txt", 'a') as fileout:
fileout.write(
"Thresholded: {ich:d}, {pl:4.2f}, {ph:4.2f} \n".format(
ich=ichannel, pl=pc_thresh, ph=pc_high))
# Plot input distribution
fig, axs = plt.subplots(2, 2, figsize=(9, 9))
axs[0, 0].set_title("Original Image")
axs[0, 0].imshow(image_in[ichannel, :, :, 70], cmap='gray')
# axs[1,0].hist(image.flatten(), 100)
axs[1, 0].set_title("Original Image - Histogram with Mask")
if mask is None:
axs[1, 0].hist(image_in[ichannel, ...].flatten(), 200)
else:
axs[1, 0].hist(image_in[ichannel, ...][in_mask].flatten(), 200)
axs[0, 1].set_title(
"Normalised Image, Level= {level:4.1f},\n "
"Window range {in1:4.1f} to {in2:4.1f}, \n"
"{pt:4.1f} % below threshold, {ph:4.1f} % above window \n"
"Threshold= {th:4.1f}".format(level=level,
in1=input_range[0],
in2=input_range[1],
pt=pc_thresh,
ph=pc_high,
th=threshold))
axs[0, 1].imshow(imout[ichannel, :, :, 70], cmap='gray')
axs[1, 1].set_title("Normalised Image - Histogram with Mask")
if mask is None:
axs[1, 1].hist(imout[ichannel, ...].flatten(), 200)
else:
axs[1, 1].hist(imout[ichannel, ...][in_mask].flatten(), 200)
plt.show()
return imout, status