def rununlocked(_dest_href,dc_dgsfile_href,dc_density_numericunits,dc_specificheat_numericunits,dc_alphaz_numericunits,dc_alphaxy_numericunits,dc_nominal_lamina_thickness_numericunits,dc_lamina_thickness_numericunits,dc_numlayers_numericunits,dc_inversion_tile_size_y_numericunits,dc_inversion_tile_size_x_numericunits,dc_inversion_channel_str,dc_inversion_startframe_numericunits,dc_flashtime_numericunits,dc_inversion_reflectors_str,xydownsample_numericunits,tikparam_numericunits,dc_cadmodel_channel_str,dc_scalefactor_x_numericunits=dc_value.numericunitsvalue(1.0,"Unitless"),dc_scalefactor_y_numericunits=dc_value.numericunitsvalue(1.0,"Unitless"),dc_numplotrows_int=3,dc_numplotcols_int=4,do_singlestep_bool=True,dc_holesadjusted_xmltree=None,dc_source_approx_dx_numericunits=None,dc_source_approx_dy_numericunits=None):
tikparam=tikparam_numericunits.value()
dc_prefix_str="greensinversion_"
reslist=[]
if tikparam==0.0:
tikparam=None # 0 and disabled are equivalent
pass
#rho=float(1.555e3) # kg/m^3
#c=float(850.0) # J/(kg* deg K)
rho=dc_density_numericunits.value('kg/m^3')
c=dc_specificheat_numericunits.value('J/(kg*K)')
# alpha units are m^2/s
#alphaz=float(.54e-6) # average value from measurements (Thermal_Properties.ods 11/25/15, averaging in-plane value from 90deg specimen and flash method values)
alphaz=dc_alphaz_numericunits.value('m^2/s')
#alphaxy=float(3.00e-6) # best evaluation based on Thermal_Properties.ods 3/19/16 based on 0/90 and quasi-isotropic layups
alphaxy=dc_alphaxy_numericunits.value('m^2/s')
# Lamina thickness based on thermal_properties.ods average thickness of 8.05 mm for 3(?) layers of 16 plies
# nominal_lamina_thickness=8.05e-3/(3.0*16.0)
nominal_lamina_thickness=dc_nominal_lamina_thickness_numericunits.value('m')
# Load input file
# NOTE: When changing input file:
# 1. Verify flashtime. Adjust as appropriate
# 2. Verify startframe. Adjust as appropriate
# 3. Execute file load code (below) and evaluate
# a) XStepMeters (must match dx)
# b) YStepMeters (must match dy)
# c) TStep (must match dt)
# d) bases[2][startframe]-flashtrigtime (must match t0)
# e) bases[2][startframe:endframe].shape[0] (must match nt)
# 4. Adjust dx, dy, dt, t0, and/or nt to satisfy above criteria
# 5. Once adjusted, assert()s below should pass.
inputfile=dc_dgsfile_href.getpath() # was "/tmp/CA-1_Bottom_2015_11_19_undistorted_orthographic.dgs"
(inputfile_basename,inputfile_ext) = posixpath.splitext(dc_dgsfile_href.get_bare_unquoted_filename())
if inputfile_ext==".bz2" or inputfile_ext==".gz": # .dgs.bz2 or .dgs.gz
orig_inputfile_basename = inputfile_basename
inputfile_basename=posixpath.splitext(orig_inputfile_basename)[0]
inputfile_ext = posixpath.splitext(orig_inputfile_basename)[1] + inputfile_ext
pass
#flashtrigtime=0.2 # seconds -- from pequod system
#flashtime=flashtrigtime+1.0/100.0 # add 1/100th second delay of flash peak (wild guess!)
flashtime=dc_flashtime_numericunits.value('s')
#channel="DiffStack"
channel=dc_inversion_channel_str
# frame #165: Time relative to trigger = bases[2][165]-flashtrigtime
# = 0.052869999999999973
#startframe=13 # zero-based, not one-based
startframe=int(round(dc_inversion_startframe_numericunits.value('unitless')))
(junkmd,wfmdict)=dgf.loadsnapshot(inputfile,memmapok=True)
channel3d = "Proj" + dc_inversion_channel_str[:-4] # Proj + diffstack channel with _tex stripped
objframe=coordframe()
(obj, TexChanPrefix) = ndepart_from_dataguzzler_wfm(wfmdict[channel3d],wfmdict,objframe)
channel_weights=channel+"_weights"
if channel_weights not in wfmdict:
channel_weights = None
pass
(ndim,DimLen,IniVal,Step,bases)=dg_eval.geom(wfmdict[channel],raw=True)
(ndim,Coord,Units,AmplCoord,AmplUnits)=dg_eval.axes(wfmdict[channel],raw=True)
XIniValMeters=dc_value.numericunitsvalue(IniVal[0],Units[0]).value('m')
YIniValMeters=dc_value.numericunitsvalue(IniVal[1],Units[1]).value('m')
# Apply scaling factor to XStepMeters (note that Coord, above, is not corrected!!!)
XStepMeters=dc_value.numericunitsvalue(Step[0],Units[0]).value('m')*dc_scalefactor_x_numericunits.value()
YStepMeters=dc_value.numericunitsvalue(Step[1],Units[1]).value('m')*dc_scalefactor_y_numericunits.value()
TStep=Step[2]
(saturation_fraction,saturation_map)=greensinversion.saturationcheck(wfmdict[channel].data.transpose((2,1,0)),startframe)
if saturation_fraction > .2:
raise ValueError("greensinversionstep: ERROR: %.1f%% of pixels are saturated at least once beyond start frame!" % (saturation_fraction*100.0))
if saturation_fraction > .02:
sys.stderr.write("greensinversionstep: WARNING: %.1f%% of pixels are saturated at least once beyond start frame!\n" % (saturation_fraction*100.0))
pass
# Apply spatial downsampling to keep inversion complexity under control
#xydownsample=2
xydownsample=int(round(xydownsample_numericunits.value("unitless")))
# reflectors is a tuple of (z,ny,nx) tuples representing
# possible z values for reflectors and how many y and x pieces
# they should be split into.
# it should be ordered from the back surface towards the
# front surface.
# reflectors is (depth, reflector_ny,reflector_nx)
# # need pre-calculation of z_bnd to determine reflectors
# z_bnd=np.arange(nz+1,dtype='d')*dz # z boundary starts at zero
# reflectors=( (z_bnd[15],4,4),
# (z_bnd[9],4,4),
# (z_bnd[5],6,6),
# (z_bnd[2],10,10))
reflectors_float=ast.literal_eval(dc_inversion_reflectors_str)
# reflectors can just be reflectors_float but this is here to avoid
# some temporary recalculations 3/29/16
reflectors=tuple([ (np.float64(reflector[0]),reflector[1],reflector[2]) for reflector in reflectors_float])
deepest_tstar = reflectors[0][0]**2.0/(np.pi*alphaz)
endframe = np.argmin(np.abs(bases[2]-flashtime-deepest_tstar*2.0)) # see also generateinversionsteps() call to timelimitmatrix()
# step sizes for inversion
dx=XStepMeters*1.0*xydownsample
dy=YStepMeters*1.0*xydownsample
dt=TStep
t0=bases[2][startframe]-flashtime
nt=bases[2][startframe:endframe].shape[0]
dz=nominal_lamina_thickness # use nominal value so we don't recalculate everything for each sample
# These now satisfied by definition
#assert(XStepMeters==dx)
#assert(YStepMeters==dy)
#assert(TStep==dt)
#assert(bases[2][startframe]-flashtrigtime==t0) # Start time matches NOTE.... CHANGED FROM flashtrigtime to flashtime
#assert(bases[2][startframe:].shape[0]==nt) # Number of frames match
# These are parameters for the reconstruction, not the expermental data
#nz=16 # NOTE: nz*dz should match specimen thickness
nz=int(round(dc_numlayers_numericunits.value('unitless')))
# size of each tile for tiled inversion
#maxy=38.0e-3
#maxx=36.0e-3
maxy=dc_inversion_tile_size_y_numericunits.value('m')
maxx=dc_inversion_tile_size_x_numericunits.value('m')
source_approx_dy=None
source_approx_dx=None
if dc_source_approx_dy_numericunits is not None:
source_approx_dy=dc_source_approx_dy_numericunits.value('m')
pass
if dc_source_approx_dx_numericunits is not None:
source_approx_dx=dc_source_approx_dx_numericunits.value('m')
pass
greensconvolution_params=read_greensconvolution()
greensconvolution_params.get_opencl_context("GPU",None)
#(kx,ky,kz,
# ny,nx,
# z,y,x,
# zgrid,ygrid,xgrid,
# z_bnd,y_bnd,x_bnd,
# flashsourcevecs,
# reflectorsourcevecs,
# depths,tstars,
# conditions,prevconditions,prevscaledconditions,
# rowselects,
# inversions,
# inversionsfull,
# inverses,
# nresults,
# ss_rowselects,
# ss_inversions,
# ss_inversionsfull,
# ss_inverses,
# ss_nresults)=greensinversion.greensinversion_lookup(cache_dir,rho,c,alphaz,alphaxy,dz,dy,dx,nz,maxy,maxx,t0,dt,nt,reflectors)
(kx,ky,kz,
ny,nx,
y,x,
ygrid,xgrid,
y_bnd,x_bnd,
num_sources_y,num_sources_x,
trange,
rowscaling,
flashsourcecolumnscaling,flashsourcevecs,
reflectorcolumnscaling,reflectorsourcevecs,
depths,tstars,
conditions,prevconditions,prevscaledconditions,
rowselects,inversions,inversionsfull,inverses,nresults)=greensinversion.setupinversionprob(rho,c,alphaz,alphaxy,dy,dx,maxy,maxx,t0,dt,nt,reflectors,source_approx_dy=source_approx_dy,source_approx_dx=source_approx_dx)
# can view individual source maps with
# reflectorsourcevecs[:,0].reshape(ny,nx,nt),
# e.g. imshow(reflectorsourcevecs[:,5].reshape(ny,nx,nt)[:,:,200])
#pl.figure(1)
#pl.clf()
#pl.imshow(reflectorsourcevecs[0][:,5].reshape(ny,nx,nt)[:,:,200])
#pl.figure(2)
#pl.clf()
#pl.imshow(reflectorsourcevecs[1][:,5].reshape(ny,nx,nt)[:,:,200])
#print("Generating inversion steps")
#
#(rowselects,inversions,inversionsfull,inverses,nresults)=greensinversion.generateinversionsteps(rowscaling,flashsourcecolumnscaling,flashsourcevecs,reflectorcolumnscaling,reflectorsourcevecs,tstars,ny,nx,trange,depths)
if do_singlestep_bool:
print("Generating single-step inversion")
(ss_rowselects,ss_inversions,ss_inversionsfull,ss_inverses,ss_nresults)=greensinversion.generatesinglestepinversion(rowscaling,flashsourcecolumnscaling,flashsourcevecs,reflectorcolumnscaling,reflectorsourcevecs,tstars,ny,nx,trange,depths)
pass
# To plot:
# loglog(trange+dt/2,T[20,20,:])
# imshow(T[:,:,200]
# Break object into tiles, perform inversion on each tile
(minyminx_corners,yranges,xranges,contributionprofiles)=greensinversion.build_tiled_rectangle(ny,nx,dy,dx,reflectors,wfmdict[channel].data.transpose((2,1,0)),xydownsample)
inputmats = [ wfmdict[channel].data[(xidx*xydownsample):((xidx+nx)*xydownsample):xydownsample,(yidx*xydownsample):((yidx+ny)*xydownsample):xydownsample,startframe:endframe].transpose((2,1,0)) for (yidx,xidx) in minyminx_corners ] # transpose to convert dataguzzler axis ordering (x,y,t) to greensinversion ordering (t,y,x)
print("Filling holes...")
inputmats_holesfilled = [ greensinversion.fillholes.fillholes_flat(inputmat) for inputmat in inputmats ]
print("Done filling holes.")
parallelevaluate=False # GPU is currently slightly SLOWER here (WHY?) so we don't use it
if parallelevaluate:
inversionevalfunc=greensinversion.inversion.parallelperforminversionsteps
OpenCL_CTX=greensconvolution_params.get_opencl_context() #greensinversion.inversion.Get_OpenCL_Context()
pass
else:
inversionevalfunc=greensinversion.inversion.serialperforminversionsteps
OpenCL_CTX=None
pass
nextfignum=1
# tikparam diagnostic plots (multi-step)
pl.figure(nextfignum)
pl.clf()
for inversioncnt in range(len(inversions)):
pl.plot(inverses[inversioncnt][1])
pass
pl.xlabel('Singular value index')
pl.ylabel('Magnitude')
nextfignum+=1
if do_singlestep_bool:
pl.figure(nextfignum)
pl.clf()
pl.plot(ss_inverses[0][1])
pl.xlabel('Singular value index (single step)')
pl.ylabel('Magnitude')
nextfignum+=1
pass
# scaled tikparam
#raise ValueError("foo!")
#z_reference=reflectors[-1][0] # z coordinate of shallowest reflectors (recall reflectors are deepest first)
#scaledtikparams=greensinversion.scale_tikparam(tikparam,z_reference,reflectors)
#if tikparam is not None:
# # tikparam scaled diagnostic plot (multi-step)
# pl.figure(nextfignum)
# pl.clf()
# for inversioncnt in range(len(inversions)):
# pl.plot(inverses[inversioncnt][1] * (tikparam/scaledtikparams[inversioncnt])) # * z_values[inversioncnt]/z_reference)
# pass
# pl.xlabel('Scaled singular value index')
# pl.ylabel('Magnitude')
# nextfignum+=1
# pass
(inversioncoeffs_list,errs_list,tikparams_list) = inversionevalfunc(OpenCL_CTX,
rowselects,
inversions,
inversionsfull,
inverses,
nresults,
inputmats_holesfilled,
tikparam)
fullinverse=np.zeros((len(reflectors)+1,wfmdict[channel].data.shape[1]//xydownsample,wfmdict[channel].data.shape[0]//xydownsample),dtype='d')
fullinverse_x_bnd=IniVal[0]-Step[0]*xydownsample/2.0 + np.arange(DimLen[0]//xydownsample+1,dtype='d')*Step[0]*xydownsample
fullinverse_y_bnd=IniVal[1]-Step[1]*xydownsample/2.0 + np.arange(DimLen[1]//xydownsample+1,dtype='d')*Step[1]*xydownsample
for tile_idx in range(len(minyminx_corners)):
(yidx,xidx)=minyminx_corners[tile_idx]
fullinverse[:,yidx:(yidx+ny),xidx:(xidx+nx)] += greensinversion.buildconcreteinverse(inversioncoeffs_list[tile_idx],reflectors,ygrid,xgrid,y_bnd,x_bnd,ny,nx,num_sources_y,num_sources_x)*contributionprofiles[tile_idx]
pass
# raise ValueError("Debugging!")
if do_singlestep_bool:
(ss_inversioncoeffs_list,ss_errs_list,ss_tikparams_list) = inversionevalfunc(OpenCL_CTX,
ss_rowselects,
ss_inversions,
ss_inversionsfull,
ss_inverses,
ss_nresults,
inputmats_holesfilled,
tikparam)
ss_fullinverse=np.zeros((len(reflectors)+1,wfmdict[channel].data.shape[1]//xydownsample,wfmdict[channel].data.shape[0]//xydownsample),dtype='d')
for tile_idx in range(len(minyminx_corners)):
(yidx,xidx)=minyminx_corners[tile_idx]
ss_fullinverse[:,yidx:(yidx+ny),xidx:(xidx+nx)] += greensinversion.buildconcreteinverse(ss_inversioncoeffs_list[tile_idx],reflectors,ygrid,xgrid,y_bnd,x_bnd,ny,nx,num_sources_y,num_sources_x)*contributionprofiles[tile_idx]
pass
# for tile_idx in range(len(minyminx_corners)):
# (yidx,xidx)=minyminx_corners[tile_idx]
# #
# (ss_inversioncoeffs,ss_residual,errs,ss_tikparams)=greensinversion.performinversionsteps(ss_rowselects,ss_inversions,ss_inversionsfull,ss_inverses,ss_nresults,wfmdict[channel].data[(xidx*xydownsample):((xidx+nx)*xydownsample):xydownsample,(yidx*xydownsample):((yidx+ny)*xydownsample):xydownsample,startframe:endframe].transpose((2,1,0)),tikparam) # transpose to convert dataguzzler axis ordering (x,y,t) to greensinversion ordering (t,y,x)
# #
# ss_concreteinverse=greensinversion.buildconcreteinverse(ss_inversioncoeffs,reflectors,ygrid,xgrid,y_bnd,x_bnd,ny,nx)
# # concreteinverse is (len(reflectors)+1,ny,nx)... first layer is surface
# # ... accumulate contributions of each tile to full inverse
# ss_fullinverse[:,yidx:(yidx+ny),xidx:(xidx+nx)] += ss_concreteinverse*contributionprofiles[tile_idx]
# pass
pass
(fig,subplots,images)=greensinversion.plotconcreteinverse(nextfignum,dc_numplotrows_int,dc_numplotcols_int,saturation_map,fullinverse,reflectors,-10000.0,30000.0,fullinverse_y_bnd,fullinverse_x_bnd,num_sources_y,num_sources_x)
nextfignum+=1
if tikparam is None:
outpng_fname="%s_greensinversion.png" % (inputfile_basename)
movieoutdirname="%s_greensinversion_movie/" % (inputfile_basename)
movieoutfilename="%s_greensinversion_movie_depth_%%05.2f.png" % (inputfile_basename)
pass
else:
outpng_fname="%s_greensinversion_tik_%g.png" % (inputfile_basename,tikparam)
movieoutdirname="%s_greensinversion_tik_%g_movie/" % (inputfile_basename,tikparam)
movieoutfilename="%s_greensinversion_tik_%g_movie_depth_%%05.2f.png" % (inputfile_basename,tikparam)
pass
outpng_href=dc_value.hrefvalue(quote(outpng_fname),_dest_href)
fig.savefig(outpng_href.getpath())
reslist.append( (("dc:greensinversion_figure",{ "tikparam": str(tikparam)}), outpng_href))
movieoutdirhref=dc_value.hrefvalue(quote(movieoutdirname),contexthref=_dest_href)
(nextfignum,plots,images,plothrefs,depths) = greensinversion.inversion.plotconcreteinversemovie(nextfignum,movieoutdirhref,movieoutfilename,saturation_map,fullinverse,reflectors,-10000.0,30000.0,fullinverse_y_bnd,fullinverse_x_bnd,num_sources_y,num_sources_x,dpi=300)
if dc_holesadjusted_xmltree is not None:
for plot in plots:
ax=plot.gca()
ax.xaxis.label.set_size(20)
ax.yaxis.label.set_size(20)
ax.title.set_size(20)
pass
# Add hole drawings for paper
holesdoc=dc_holesadjusted_xmltree.get_xmldoc()
for hole in holesdoc.xpath("(dc:hole|dc:annulus)[@num]"):
numstr=holesdoc.xpathcontext(hole,"@num")[0]
numnum=re.match(r"""(\d+)""",numstr).group(1)
if hole.tag.endswith("hole") and len(holesdoc.xpath("dc:annulus[translate(@num,translate(@num,'0123456789',''),'') = '%s']" % (numnum))) > 0:
# if there is an annulus with this number, ignore the hole.
continue
holecenterx=dc_value.numericunitsvalue.fromxml(holesdoc,holesdoc.child(hole,"dc:xpos"))
holecentery=dc_value.numericunitsvalue.fromxml(holesdoc,holesdoc.child(hole,"dc:ypos"))
holediameter=dc_value.numericunitsvalue.fromxml(holesdoc,holesdoc.child(hole,"dc:diameter"))
holeradius=holediameter/2.0
holedepth=dc_value.numericunitsvalue.fromxml(holesdoc,holesdoc.child(hole,"dc:depth"))
for plot in plots:
ax=plot.gca()
circ=pl.Circle((holecenterx.inunits('mm').value(),
holecentery.inunits('mm').value()),
holeradius.inunits('mm').value(),
facecolor='none')
ax.add_artist(circ)
pass
pass
for plotcnt in range(len(plots)):
# rewrite plot files
plot=plots[plotcnt]
plothref=plothrefs[plotcnt]
plot.savefig(plothref.getpath(),dpi=300)
pass
pass
for cnt in range(len(plothrefs)):
reslist.append( (("dc:greensinversion_movie_frame",{ "tikparam": str(tikparam),"depth":str(depths[cnt])}), plothrefs[cnt]))
pass
if do_singlestep_bool:
(ss_fig,ss_subplots,ss_images)=greensinversion.plotconcreteinverse(nextfignum,dc_numplotrows_int,dc_numplotcols_int,saturation_map,ss_fullinverse,reflectors,-10000.0,30000.0,fullinverse_y_bnd,fullinverse_x_bnd,num_sources_y,num_sources_x)
nextfignum+=1
if tikparam is None:
ss_outpng_fname="%s_ss_greensinversion.png" % (inputfile_basename)
ss_movieoutdirname="%s_ss_greensinversion_movie/" % (inputfile_basename)
ss_movieoutfilename="%s_ss_greensinversion_movie_depth_%%05.2f.png" % (inputfile_basename)
pass
else:
ss_outpng_fname="%s_ss_greensinversion_tik_%g.png" % (inputfile_basename,tikparam)
ss_movieoutdirname="%s_ss_greensinversion_tik_%g_movie/" % (inputfile_basename,tikparam)
ss_movieoutfilename="%s_ss_greensinversion_tik_%g_movie_depth_%%05.2f.png" % (inputfile_basename,tikparam)
pass
ss_outpng_href=dc_value.hrefvalue(quote(ss_outpng_fname),_dest_href)
ss_fig.savefig(ss_outpng_href.getpath())
reslist.append( (("dc:greensinversion_singlestep_figure", {"tikparam": str(tikparam) }), ss_outpng_href) )
ss_movieoutdirhref=dc_value.hrefvalue(quote(ss_movieoutdirname),contexthref=_dest_href)
(nextfignum,ss_plots,ss_images,ss_plothrefs,ss_depths) = greensinversion.inversion.plotconcreteinversemovie(nextfignum,ss_movieoutdirhref,ss_movieoutfilename,saturation_map,ss_fullinverse,reflectors,-10000.0,30000.0,fullinverse_y_bnd,fullinverse_x_bnd,num_sources_y,num_sources_x,resolution=300)
for cnt in range(len(ss_plothrefs)):
reslist.append( (("dc:ss_greensinversion_movie_frame",{ "tikparam": str(tikparam),"depth":str(ss_depths[cnt])}), ss_plothrefs[cnt]))
pass
pass
outwfmdict={}
outwfmdict[dc_cadmodel_channel_str]=copy.deepcopy(wfmdict[dc_cadmodel_channel_str])
SplitTextureChans=dgm.GetMetaDatumWIStr(wfmdict[dc_cadmodel_channel_str],"TextureChans","").split("|")
PrefixedTextureChans="|".join([ dc_prefix_str + TexChanPrefix + TexChan for TexChan in SplitTextureChans ])
gi_3d=dg.wfminfo()
#gi_3d.Name=dc_prefix_str+dc_cadmodel_channel_str
gi_3d.Name="Proj"+dc_prefix_str+TexChanPrefix+dc_cadmodel_channel_str
gi_3d.dimlen=np.array((1,),dtype='i8')
gi_3d.data=np.array((1,),dtype='f')
dgm.AddMetaDatumWI(gi_3d,dgm.MetaDatum("VRML97GeomRef",dc_cadmodel_channel_str))
dgm.AddMetaDatumWI(gi_3d,dgm.MetaDatum("X3DGeomRef",dc_cadmodel_channel_str))
#texchanprefix=gi_3d.Name[:gi_3d.Name.find(dc_unprefixed_texname_str)]
dgm.AddMetaDatumWI(gi_3d,dgm.MetaDatum("TexChanPrefix",dc_prefix_str+TexChanPrefix))
dgm.AddMetaDatumWI(gi_3d,dgm.MetaDatum("TextureChans",PrefixedTextureChans))
outwfmdict[gi_3d.Name]=gi_3d
outwfm=dg.wfminfo()
#outwfm.Name="greensinversion"
outwfm.Name=dc_prefix_str+dc_inversion_channel_str
outwfmdict[outwfm.Name]=outwfm
# Shift IniVals according to xydownsample:
# IniVal[0] is X coordinate of center of corner pixel of undownsampled image
# IniVal[0] is X coordinate of center of corner pixel downsampled image
# but that pixel is twice as big, so the corner of the image itself
# has changed!
dgm.AddMetaDatumWI(outwfm,dgm.MetaDatum("IniVal1",IniVal[0]))
dgm.AddMetaDatumWI(outwfm,dgm.MetaDatum("IniVal2",IniVal[1]))
dgm.AddMetaDatumWI(outwfm,dgm.MetaDatum("Step1",XStepMeters*xydownsample))
dgm.AddMetaDatumWI(outwfm,dgm.MetaDatum("Step2",YStepMeters*xydownsample))
dgm.AddMetaDatumWI(outwfm,dgm.MetaDatum("Coord1",Coord[0]))
dgm.AddMetaDatumWI(outwfm,dgm.MetaDatum("Coord2",Coord[1]))
dgm.AddMetaDatumWI(outwfm,dgm.MetaDatum("Units1",Units[0]))
dgm.AddMetaDatumWI(outwfm,dgm.MetaDatum("Units2",Units[1]))
dgm.AddMetaDatumWI(outwfm,dgm.MetaDatum("IniVal3",0.0))
dgm.AddMetaDatumWI(outwfm,dgm.MetaDatum("Step3",1.0))
dgm.AddMetaDatumWI(outwfm,dgm.MetaDatum("Coord3","Depth Index"))
dgm.AddMetaDatumWI(outwfm,dgm.MetaDatum("Units3","unitless"))
dgm.AddMetaDatumWI(outwfm,dgm.MetaDatum("AmplCoord","Heating intensity"))
dgm.AddMetaDatumWI(outwfm,dgm.MetaDatum("AmplUnits","J/m^2"))
# Copy landmark metadata
LandmarkMD = [ MDName for MDName in list(wfmdict[channel].MetaData.keys()) if MDName.startswith("LANDMARK_") ]
for LandmarkName in LandmarkMD:
dgm.AddMetaDatumWI(outwfm,copy.deepcopy(wfmdict[channel].MetaData[LandmarkName]))
pass
if channel_weights is not None:
#outwfm_weights=copy.deepcopy(wfmdict[channel_weights])#dg.wfminfo()
#outwfm_weights.Name="greensinversion_weights"
outwfm_weights=dg.wfminfo()
outwfm_weights.Name=dc_prefix_str+dc_inversion_channel_str+"_weights"
outwfm_weights.data=wfmdict[channel_weights].data[::xydownsample,::xydownsample]
outwfm_weights.dimlen=np.array(outwfm_weights.data.shape)
outwfm_weights.ndim=2
outwfm_weights.MetaData=copy.deepcopy(outwfm.MetaData)
dgm.AddMetaDatumWI(outwfm_weights,dgm.MetaDatum("AmplCoord","Weighting"))
dgm.AddMetaDatumWI(outwfm_weights,dgm.MetaDatum("AmplUnits","Unitless"))
outwfmdict[outwfm_weights.Name]=outwfm_weights
pass
if do_singlestep_bool:
ss_outwfm=copy.deepcopy(outwfm)
ss_outwfm.Name="ss_greensinversion"
outwfmdict[ss_outwfm.Name]=ss_outwfm
pass
# dgs file is written in (X,Y,Z) fortran order, so we write
# dimlen in reverse order and transpose the data
outwfm.ndim=3
outwfm.dimlen=np.array(fullinverse.shape[::-1])
outwfm.data=fullinverse.transpose().astype(np.float32)
outwfm.NeedData=False
outwfm.NeedMetaData=False
outwfm.HaveData=True
outwfm.HaveMetaData=True
outwfm_saturationmap=dg.wfminfo()
outwfm_saturationmap.Name="saturation_map"
outwfmdict[outwfm_saturationmap.Name]=outwfm_saturationmap
outwfm_saturationmap.dimlen=np.array(saturation_map.shape[::-1])
outwfm_saturationmap.data=saturation_map.transpose().astype(np.float32)
outwfm_saturationmap.ndim=outwfm_saturationmap.dimlen.shape[0]
outwfm_saturationmap.NeedData=False
outwfm_saturationmap.NeedMetaData=False
outwfm_saturationmap.HaveData=True
outwfm_saturationmap.HaveMetaData=True
outwfm_saturationmap.MetaData=copy.deepcopy(outwfm.MetaData)
if do_singlestep_bool:
ss_outwfm.ndim=3
ss_outwfm.dimlen=np.array(ss_fullinverse.shape[::-1])
ss_outwfm.data=ss_fullinverse.transpose().astype(np.float32)
ss_outwfm.NeedData=False
ss_outwfm.NeedMetaData=False
ss_outwfm.HaveData=True
ss_outwfm.HaveMetaData=True
pass
if tikparam is None:
outdgs_fname="%s_greensinversion.dgs" % (inputfile_basename)
pass
else:
outdgs_fname="%s_greensinversion_tik_%g.dgs" % (inputfile_basename,tikparam)
pass
outdgs_href=dc_value.hrefvalue(quote(outdgs_fname),_dest_href)
dgf.savesnapshot(outdgs_href.getpath(),outwfmdict)
reslist.append( (("dc:greensinversion_dgsfile",{"tikparam": str(tikparam)}), outdgs_href))
if do_singlestep_bool:
pass
#
# greensconvolution_params.get_opencl_context()
# tile_idx=14
# (yidx,xidx)=minyminx_corners[tile_idx]
#
# inputmats=[wfmdict[channel].data[(xidx*xydownsample):((xidx+nx)*xydownsample):xydownsample,(yidx*xydownsample):((yidx+ny)*xydownsample):xydownsample,startframe:endframe].transpose((2,1,0))]
# greeninversion.inversion.parallelperforminversionsteps(greensconvolution_params.OpenCL_CTX,rowselects,inversions,inversionsfull,inverses,nresults,inputmats,None)
return reslist
pl.figure(2)
pl.clf()
pl.imshow(reflectorsourcevecs[1][:, 5].reshape(nt, ny, nx)[200, :, :])
print("Generating inversion steps")
(rowselects, inversions, inversionsfull,
inverses, nresults) = greensinversion.generateinversionsteps(
rowscaling, flashsourcecolumnscaling, flashsourcevecs,
reflectorcolumnscaling, reflectorsourcevecs, tstars, ny, nx, trange,
depths)
print("Generating single-step inversion")
(ss_rowselects, ss_inversions, ss_inversionsfull, ss_inverses,
ss_nresults) = greensinversion.generatesinglestepinversion(
rowscaling, flashsourcecolumnscaling, flashsourcevecs,
reflectorcolumnscaling, reflectorsourcevecs, tstars, ny, nx, trange,
depths)
# diagnostic plots for finding Tikhonov parameter
pl.figure(3)
pl.clf()
for inversioncnt in range(len(inversions)):
pl.plot(inverses[inversioncnt][1])
pass
pl.xlabel('Singular value index')
pl.ylabel('Magnitude')
pl.figure(4)
pl.clf()
pl.plot(ss_inverses[0][1])
pl.xlabel('Singular value index (single step)')
def rununlocked(_dest_href,
dc_dgsfile_href,
dc_density_numericunits,
dc_specificheat_numericunits,
dc_alphaz_numericunits,
dc_alphaxy_numericunits,
dc_nominal_lamina_thickness_numericunits,
dc_lamina_thickness_numericunits,
dc_numlayers_numericunits,
dc_inversion_tile_size_y_numericunits,
dc_inversion_tile_size_x_numericunits,
dc_inversion_channel_str,
dc_inversion_startframe_numericunits,
dc_flashtime_numericunits,
dc_inversion_reflectors_str,
xydownsample_numericunits,
tikparam_numericunits,
dc_cadmodel_channel_str,
dc_scalefactor_x_numericunits=dc_value.numericunitsvalue(
1.0, "Unitless"),
dc_scalefactor_y_numericunits=dc_value.numericunitsvalue(
1.0, "Unitless"),
dc_numplotrows_int=3,
dc_numplotcols_int=4,
do_singlestep_bool=True,
dc_holesadjusted_xmltree=None,
dc_source_approx_dx_numericunits=None,
dc_source_approx_dy_numericunits=None):
tikparam = tikparam_numericunits.value()
dc_prefix_str = "greensinversion_"
reslist = []
if tikparam == 0.0:
tikparam = None # 0 and disabled are equivalent
pass
#rho=float(1.555e3) # kg/m^3
#c=float(850.0) # J/(kg* deg K)
rho = dc_density_numericunits.value('kg/m^3')
c = dc_specificheat_numericunits.value('J/(kg*K)')
# alpha units are m^2/s
#alphaz=float(.54e-6) # average value from measurements (Thermal_Properties.ods 11/25/15, averaging in-plane value from 90deg specimen and flash method values)
alphaz = dc_alphaz_numericunits.value('m^2/s')
#alphaxy=float(3.00e-6) # best evaluation based on Thermal_Properties.ods 3/19/16 based on 0/90 and quasi-isotropic layups
alphaxy = dc_alphaxy_numericunits.value('m^2/s')
# Lamina thickness based on thermal_properties.ods average thickness of 8.05 mm for 3(?) layers of 16 plies
# nominal_lamina_thickness=8.05e-3/(3.0*16.0)
nominal_lamina_thickness = dc_nominal_lamina_thickness_numericunits.value(
'm')
# Load input file
# NOTE: When changing input file:
# 1. Verify flashtime. Adjust as appropriate
# 2. Verify startframe. Adjust as appropriate
# 3. Execute file load code (below) and evaluate
# a) XStepMeters (must match dx)
# b) YStepMeters (must match dy)
# c) TStep (must match dt)
# d) bases[2][startframe]-flashtrigtime (must match t0)
# e) bases[2][startframe:endframe].shape[0] (must match nt)
# 4. Adjust dx, dy, dt, t0, and/or nt to satisfy above criteria
# 5. Once adjusted, assert()s below should pass.
inputfile = dc_dgsfile_href.getpath(
) # was "/tmp/CA-1_Bottom_2015_11_19_undistorted_orthographic.dgs"
(inputfile_basename, inputfile_ext) = posixpath.splitext(
dc_dgsfile_href.get_bare_unquoted_filename())
if inputfile_ext == ".bz2" or inputfile_ext == ".gz": # .dgs.bz2 or .dgs.gz
orig_inputfile_basename = inputfile_basename
inputfile_basename = posixpath.splitext(orig_inputfile_basename)[0]
inputfile_ext = posixpath.splitext(
orig_inputfile_basename)[1] + inputfile_ext
pass
#flashtrigtime=0.2 # seconds -- from pequod system
#flashtime=flashtrigtime+1.0/100.0 # add 1/100th second delay of flash peak (wild guess!)
flashtime = dc_flashtime_numericunits.value('s')
#channel="DiffStack"
channel = dc_inversion_channel_str
# frame #165: Time relative to trigger = bases[2][165]-flashtrigtime
# = 0.052869999999999973
#startframe=13 # zero-based, not one-based
startframe = int(
round(dc_inversion_startframe_numericunits.value('unitless')))
(junkmd, wfmdict) = dgf.loadsnapshot(inputfile, memmapok=True)
channel3d = "Proj" + dc_inversion_channel_str[:
-4] # Proj + diffstack channel with _tex stripped
objframe = coordframe()
(obj,
TexChanPrefix) = ndepart_from_dataguzzler_wfm(wfmdict[channel3d], wfmdict,
objframe)
channel_weights = channel + "_weights"
if channel_weights not in wfmdict:
channel_weights = None
pass
(ndim, DimLen, IniVal, Step, bases) = dg_eval.geom(wfmdict[channel],
raw=True)
(ndim, Coord, Units, AmplCoord, AmplUnits) = dg_eval.axes(wfmdict[channel],
raw=True)
XIniValMeters = dc_value.numericunitsvalue(IniVal[0], Units[0]).value('m')
YIniValMeters = dc_value.numericunitsvalue(IniVal[1], Units[1]).value('m')
# Apply scaling factor to XStepMeters (note that Coord, above, is not corrected!!!)
XStepMeters = dc_value.numericunitsvalue(
Step[0], Units[0]).value('m') * dc_scalefactor_x_numericunits.value()
YStepMeters = dc_value.numericunitsvalue(
Step[1], Units[1]).value('m') * dc_scalefactor_y_numericunits.value()
TStep = Step[2]
(saturation_fraction, saturation_map) = greensinversion.saturationcheck(
wfmdict[channel].data.transpose((2, 1, 0)), startframe)
if saturation_fraction > .2:
raise ValueError(
"greensinversionstep: ERROR: %.1f%% of pixels are saturated at least once beyond start frame!"
% (saturation_fraction * 100.0))
if saturation_fraction > .02:
sys.stderr.write(
"greensinversionstep: WARNING: %.1f%% of pixels are saturated at least once beyond start frame!\n"
% (saturation_fraction * 100.0))
pass
# Apply spatial downsampling to keep inversion complexity under control
#xydownsample=2
xydownsample = int(round(xydownsample_numericunits.value("unitless")))
# reflectors is a tuple of (z,ny,nx) tuples representing
# possible z values for reflectors and how many y and x pieces
# they should be split into.
# it should be ordered from the back surface towards the
# front surface.
# reflectors is (depth, reflector_ny,reflector_nx)
# # need pre-calculation of z_bnd to determine reflectors
# z_bnd=np.arange(nz+1,dtype='d')*dz # z boundary starts at zero
# reflectors=( (z_bnd[15],4,4),
# (z_bnd[9],4,4),
# (z_bnd[5],6,6),
# (z_bnd[2],10,10))
reflectors_float = ast.literal_eval(dc_inversion_reflectors_str)
# reflectors can just be reflectors_float but this is here to avoid
# some temporary recalculations 3/29/16
reflectors = tuple([(np.float64(reflector[0]), reflector[1], reflector[2])
for reflector in reflectors_float])
deepest_tstar = reflectors[0][0]**2.0 / (np.pi * alphaz)
endframe = np.argmin(
np.abs(bases[2] - flashtime - deepest_tstar * 2.0)
) # see also generateinversionsteps() call to timelimitmatrix()
# step sizes for inversion
dx = XStepMeters * 1.0 * xydownsample
dy = YStepMeters * 1.0 * xydownsample
dt = TStep
t0 = bases[2][startframe] - flashtime
nt = bases[2][startframe:endframe].shape[0]
dz = nominal_lamina_thickness # use nominal value so we don't recalculate everything for each sample
# These now satisfied by definition
#assert(XStepMeters==dx)
#assert(YStepMeters==dy)
#assert(TStep==dt)
#assert(bases[2][startframe]-flashtrigtime==t0) # Start time matches NOTE.... CHANGED FROM flashtrigtime to flashtime
#assert(bases[2][startframe:].shape[0]==nt) # Number of frames match
# These are parameters for the reconstruction, not the expermental data
#nz=16 # NOTE: nz*dz should match specimen thickness
nz = int(round(dc_numlayers_numericunits.value('unitless')))
# size of each tile for tiled inversion
#maxy=38.0e-3
#maxx=36.0e-3
maxy = dc_inversion_tile_size_y_numericunits.value('m')
maxx = dc_inversion_tile_size_x_numericunits.value('m')
source_approx_dy = None
source_approx_dx = None
if dc_source_approx_dy_numericunits is not None:
source_approx_dy = dc_source_approx_dy_numericunits.value('m')
pass
if dc_source_approx_dx_numericunits is not None:
source_approx_dx = dc_source_approx_dx_numericunits.value('m')
pass
greensconvolution_params = read_greensconvolution()
greensconvolution_params.get_opencl_context("GPU", None)
#(kx,ky,kz,
# ny,nx,
# z,y,x,
# zgrid,ygrid,xgrid,
# z_bnd,y_bnd,x_bnd,
# flashsourcevecs,
# reflectorsourcevecs,
# depths,tstars,
# conditions,prevconditions,prevscaledconditions,
# rowselects,
# inversions,
# inversionsfull,
# inverses,
# nresults,
# ss_rowselects,
# ss_inversions,
# ss_inversionsfull,
# ss_inverses,
# ss_nresults)=greensinversion.greensinversion_lookup(cache_dir,rho,c,alphaz,alphaxy,dz,dy,dx,nz,maxy,maxx,t0,dt,nt,reflectors)
kx = alphaxy * rho * c
ky = alphaxy * rho * c
kz = alphaz * rho * c
trange = t0 + np.arange(nt, dtype='d') * dt
gi_params = (rho, c, alphaz, alphaxy, dy, dx, maxy, maxx, t0, dt, nt,
reflectors, trange, greensconvolution_params)
flat_gi_grid = build_gi_grid(dy, maxy, dx, maxx)
(ny, nx, y, x, ygrid, xgrid, y_bnd, x_bnd) = flat_gi_grid
num_sources_y = 2
num_sources_x = 2
if source_approx_dy is not None or source_approx_dx is not None:
(num_sources_y,
num_sources_x) = greensinversion.num_sources(y, x, y_bnd, x_bnd,
source_approx_dy,
source_approx_dx)
pass
# can view individual source maps with
# reflectorsourcevecs[:,0].reshape(ny,nx,nt),
# e.g. imshow(reflectorsourcevecs[:,5].reshape(ny,nx,nt)[:,:,200])
#pl.figure(1)
#pl.clf()
#pl.imshow(reflectorsourcevecs[0][:,5].reshape(ny,nx,nt)[:,:,200])
#pl.figure(2)
#pl.clf()
#pl.imshow(reflectorsourcevecs[1][:,5].reshape(ny,nx,nt)[:,:,200])
# To plot:
# loglog(trange+dt/2,T[20,20,:])
# imshow(T[:,:,200]
# Break object into tiles, perform inversion on each tile
(minyminx_corners, yranges, xranges,
contributionprofiles) = greensinversion.build_tiled_rectangle(
ny, nx, dy, dx, reflectors, wfmdict[channel].data.transpose(
(2, 1, 0)), xydownsample)
inputmats = [
wfmdict[channel].data[(xidx *
xydownsample):((xidx + nx) *
xydownsample):xydownsample,
(yidx *
xydownsample):((yidx + ny) *
xydownsample):xydownsample,
startframe:endframe].transpose((2, 1, 0))
for (yidx, xidx) in minyminx_corners
] # transpose to convert dataguzzler axis ordering (x,y,t) to greensinversion ordering (t,y,x)
print("Filling holes...")
inputmats_holesfilled = [
greensinversion.fillholes.fillholes_flat(inputmat)
for inputmat in inputmats
]
print("Done filling holes.")
parallelevaluate = False # GPU is currently slightly SLOWER here (WHY?) so we don't use it
if parallelevaluate:
inversionevalfunc = greensinversion.inversion.parallelperforminversionsteps
OpenCL_CTX = greensconvolution_params.get_opencl_context(
) #greensinversion.inversion.Get_OpenCL_Context()
pass
else:
inversionevalfunc = greensinversion.inversion.serialperforminversionsteps
OpenCL_CTX = None
pass
print("Evaluating curvatures")
hires_factor = 2
curvmat = obj.implpart.surfaces[
0].intrinsicparameterization.interpolate_curvature(
obj.implpart.surfaces[0],
wfmdict[channel].data.shape[1] / xydownsample,
wfmdict[channel].data.shape[0] / xydownsample)
# curvmat is uv_channame ny x nx x 2x2 matrix representing the shape operator
curvmat_hires = obj.implpart.surfaces[
0].intrinsicparameterization.interpolate_curvature(
obj.implpart.surfaces[0],
wfmdict[channel].data.shape[1] * hires_factor // xydownsample,
wfmdict[channel].data.shape[0] * hires_factor // xydownsample)
# Set unknown curvatures to zero
curvmat[np.isnan(curvmat)] = 0.0
curvmat_hires[np.isnan(curvmat_hires)] = 0.0
# These are only nominal physical sizes (in terms of nominal dx and dy of parameterization)
curvmat_sizex = dx * wfmdict[channel].data.shape[0] / xydownsample
curvmat_sizey = dy * wfmdict[channel].data.shape[1] / xydownsample
print("Determining maximum principal curvatures")
#maxabs_princcurvs = np.max(np.abs(eigvals_broadcast_nans(curvmat)),2)
maxabs_princcurvs = np.max(np.abs(fast2x2evals(curvmat)), 2)
print("Evaluating step sizes")
stepsizemat = obj.implpart.surfaces[
0].intrinsicparameterization.interpolate_stepsizes(
obj.implpart.surfaces[0],
wfmdict[channel].data.shape[1] // xydownsample,
wfmdict[channel].data.shape[0] // xydownsample)
stepsizemat_hires = obj.implpart.surfaces[
0].intrinsicparameterization.interpolate_stepsizes(
obj.implpart.surfaces[0],
wfmdict[channel].data.shape[1] * hires_factor // xydownsample,
wfmdict[channel].data.shape[0] * hires_factor // xydownsample)
# Fill in invalid stepsizes sith dx,dy
ssm_xy_nelem = stepsizemat.shape[0] * stepsizemat.shape[1]
ssm_nan_dx = np.isnan(stepsizemat.reshape(ssm_xy_nelem, 2)[:, 0])
stepsizemat.reshape(ssm_xy_nelem, 2)[ssm_nan_dx, 0] = dx
ssm_nan_dy = np.isnan(stepsizemat.reshape(ssm_xy_nelem, 2)[:, 1])
stepsizemat.reshape(ssm_xy_nelem, 2)[ssm_nan_dx, 1] = dy
ssm_hires_xy_nelem = stepsizemat_hires.shape[0] * stepsizemat_hires.shape[1]
ssm_hires_nan_dx = np.isnan(
stepsizemat_hires.reshape(ssm_hires_xy_nelem, 2)[:, 0])
stepsizemat_hires.reshape(ssm_hires_xy_nelem, 2)[ssm_hires_nan_dx,
0] = dx / hires_factor
ssm_hires_nan_dy = np.isnan(
stepsizemat_hires.reshape(ssm_hires_xy_nelem, 2)[:, 1])
stepsizemat_hires.reshape(ssm_hires_xy_nelem, 2)[ssm_hires_nan_dy,
1] = dy / hires_factor
minimal_curvature = maxabs_princcurvs < 1.0 / (20 * reflectors[0][0])
nominal_scaling = (
(np.abs((stepsizemat[:, :, 0] - dx) / dx) < 0.05)
& # less than 5% scaling error using nominal scaling factors
(np.abs((stepsizemat[:, :, 1] - dy) / dy) < 0.05))
use_flat = minimal_curvature & nominal_scaling
# scaled tikparam
#raise ValueError("foo!")
#z_reference=reflectors[-1][0] # z coordinate of shallowest reflectors (recall reflectors are deepest first)
#scaledtikparams=greensinversion.scale_tikparam(tikparam,z_reference,reflectors)
#if tikparam is not None:
# # tikparam scaled diagnostic plot (multi-step)
# pl.figure(nextfignum)
# pl.clf()
# for inversioncnt in range(len(inversions)):
# pl.plot(inverses[inversioncnt][1] * (tikparam/scaledtikparams[inversioncnt])) # * z_values[inversioncnt]/z_reference)
# pass
# pl.xlabel('Scaled singular value index')
# pl.ylabel('Magnitude')
# nextfignum+=1
# pass
fullinverse = np.zeros(
(len(reflectors) + 1, wfmdict[channel].data.shape[1] // xydownsample,
wfmdict[channel].data.shape[0] // xydownsample),
dtype='d')
fullinverse_x_bnd = IniVal[0] - Step[0] * xydownsample / 2.0 + np.arange(
DimLen[0] // xydownsample + 1, dtype='d') * Step[0] * xydownsample
fullinverse_y_bnd = IniVal[1] - Step[1] * xydownsample / 2.0 + np.arange(
DimLen[1] // xydownsample + 1, dtype='d') * Step[1] * xydownsample
flat_tile = [
use_flat[yidx:(yidx + ny), xidx:(xidx + nx)].all()
for (yidx, xidx) in minyminx_corners
]
valid_tile = [
not ((np.isnan(curvmat[yidx:(yidx + ny),
xidx:(xidx + nx), :, :])).any())
for (yidx, xidx) in minyminx_corners
]
if channel_weights is None:
# Assume all tiles have nonzero weights
weighted_tile = [True for (yidx, xidx) in minyminx_corners]
pass
else:
# True only for tiles with a non-zero weight
weights_data = wfmdict[
channel_weights].data[::xydownsample, ::xydownsample].T
weighted_tile = [
(weights_data[yidx:(yidx + ny), xidx:(xidx + nx)] > 0.0).any()
for (yidx, xidx) in minyminx_corners
]
pass
#eval_linelength_avgcurvature_mirroredbox = lambda boxu1,boxv1,boxu2,boxv2,u1,v1,u2,v2: obj.implpart.surfaces[0].intrinsicparameterization.linelength_avgcurvature_mirroredbox_meshbased(obj.implpart.surfaces[0],curvmat_hires,stepsizemat_hires,obj.implpart.surfaces[0].intrinsicparameterization.lowerleft_meaningfulunits[0],obj.implpart.surfaces[0].intrinsicparameterization.lowerleft_meaningfulunits[1],curvmat_sizex*1.0/curvmat_hires.shape[1],curvmat_sizey*1.0/curvmat_hires.shape[0],boxu1,boxv1,boxu2,boxv2,dx,dy,u1,v1,u2,v2)
#boxu1=-0.000447803
#boxv1=-0.000447803
#boxu2=0.021643787
#boxv2=0.023434997
#u1=-0.00014926799999999998
#v1=-0.00014926799999999998
#u2=-0.001343408
#v2=-0.000746338
#if np.isnan(eval_linelength_avgcurvature_mirroredbox(boxu1,boxv1,boxu2,boxv2,u1,v1,u2,v2)).any():
# raise ValueError("NAN")
#break linelength_avgcurvature_mirroredbox_meshbased_c_one
print("Defining flat surface inversion")
# (rowscaling,flashsourcecolumnscaling,flashsourcevecs,reflectorcolumnscaling,reflectorsourcevecs,depths,tstars,conditions,prevconditions,prevscaledconditions,rowselects,inversions,inversionsfull,inverses,nresults)
flat_inversion = define_flat_inversion(gi_params, flat_gi_grid,
num_sources_y, num_sources_x)
if do_singlestep_bool:
print("Generating single-step inversion")
# should be define_flat_inversion here probably
(ss_rowselects, ss_inversions, ss_inversionsfull, ss_inverses,
ss_nresults) = greensinversion.generatesinglestepinversion(
rowscaling, flashsourcecolumnscaling, flashsourcevecs,
reflectorcolumnscaling, reflectorsourcevecs, tstars, ny, nx,
trange, depths)
pass
nextfignum = 1
# tikparam diagnostic plots (multi-step)
if False:
pl.figure(nextfignum)
pl.clf()
for inversioncnt in range(len(inversions)):
pl.plot(inverses[inversioncnt][1])
pass
pl.xlabel('Singular value index')
pl.ylabel('Magnitude')
nextfignum += 1
if do_singlestep_bool:
pl.figure(nextfignum)
pl.clf()
pl.plot(ss_inverses[0][1])
pl.xlabel('Singular value index (single step)')
pl.ylabel('Magnitude')
nextfignum += 1
pass
pass
print("Iterating over %d tiles" % (len(minyminx_corners)))
for tile_idx in range(len(minyminx_corners)):
(yidx, xidx) = minyminx_corners[tile_idx]
print("Tile %d/%d" % (tile_idx, len(minyminx_corners)))
#if tile_idx==27:
# raise ValueError("FOO!")
inputmat = inputmats_holesfilled[tile_idx]
if (flat_tile[tile_idx]
) or not valid_tile[tile_idx] or not weighted_tile[
tile_idx]: # or tile idx > 75 or tile_idx != 27 # or tile_idx < 26 or tile_idx > 28
(ny, nx, y, x, ygrid, xgrid, y_bnd, x_bnd) = flat_gi_grid
(rowscaling, flashsourcecolumnscaling, flashsourcevecs,
reflectorcolumnscaling, reflectorsourcevecs, depths, tstars,
conditions, prevconditions, prevscaledconditions, rowselects,
inversions, inversionsfull, inverses, nresults) = flat_inversion
print("inverting with flat_inversion")
pass
elif valid_tile[tile_idx]:
# build grid at this location
gi_grid = build_gi_grid(dy,
maxy,
dx,
maxx,
firstcentery=IniVal[1] + yidx * dy,
firstcenterx=IniVal[0] + xidx * dx)
(ny, nx, y, x, ygrid, xgrid, y_bnd, x_bnd) = gi_grid
try:
(rowscaling, flashsourcecolumnscaling, flashsourcevecs,
reflectorcolumnscaling, reflectorsourcevecs, depths, tstars,
conditions, prevconditions, prevscaledconditions, rowselects,
inversions, inversionsfull, inverses,
nresults) = define_curved_inversion(
gi_params,
gi_grid,
obj,
curvmat[yidx:(yidx + ny), xidx:(xidx + nx)],
stepsizemat[yidx:(yidx + ny), xidx:(xidx + nx)],
curvmat_hires,
stepsizemat_hires,
curvmat_sizex,
curvmat_sizey,
num_sources_y=num_sources_y,
num_sources_x=num_sources_x)
pass
except NotANumberError as e:
sys.stderr.write(
"WARNING: Found NAN in sourcevecs... using flat (tile idx %d; yidx=%d, xidx=%d): %s\n"
% (tile_idx, yidx, xidx, str(e)))
(ny, nx, y, x, ygrid, xgrid, y_bnd, x_bnd) = flat_gi_grid
(rowscaling, flashsourcecolumnscaling, flashsourcevecs,
reflectorcolumnscaling, reflectorsourcevecs, depths, tstars,
conditions, prevconditions, prevscaledconditions, rowselects,
inversions, inversionsfull, inverses,
nresults) = flat_inversion
#raise #!!!
pass
print("inverting with curved_inversion")
pass
else:
continue
(inversioncoeffs_list, errs_list, tikparams_list) = inversionevalfunc(
OpenCL_CTX, rowselects, inversions, inversionsfull, inverses,
nresults, [inputmat], tikparam)
fullinverse[:, yidx:(yidx + ny),
xidx:(xidx + nx)] += greensinversion.buildconcreteinverse(
inversioncoeffs_list[0], reflectors, ygrid, xgrid,
y_bnd, x_bnd, ny, nx, num_sources_y,
num_sources_x) * contributionprofiles[tile_idx]
pass
# raise ValueError("Debugging!")
print("Performing assumed-flat inversion")
print("Iterating over %d tiles" % (len(minyminx_corners)))
flatfullinverse = np.zeros(
(len(reflectors) + 1, wfmdict[channel].data.shape[1] // xydownsample,
wfmdict[channel].data.shape[0] // xydownsample),
dtype='d')
for tile_idx in range(len(minyminx_corners)):
(yidx, xidx) = minyminx_corners[tile_idx]
print("Tile %d/%d" % (tile_idx, len(minyminx_corners)))
inputmat = inputmats_holesfilled[tile_idx]
(ny, nx, y, x, ygrid, xgrid, y_bnd, x_bnd) = flat_gi_grid
(rowscaling, flashsourcecolumnscaling, flashsourcevecs,
reflectorcolumnscaling, reflectorsourcevecs, depths, tstars,
conditions, prevconditions, prevscaledconditions, rowselects,
inversions, inversionsfull, inverses, nresults) = flat_inversion
(inversioncoeffs_list, errs_list, tikparams_list) = inversionevalfunc(
OpenCL_CTX, rowselects, inversions, inversionsfull, inverses,
nresults, [inputmat], tikparam)
flatfullinverse[:, yidx:(yidx + ny), xidx:(
xidx + nx)] += greensinversion.buildconcreteinverse(
inversioncoeffs_list[0],
reflectors,
ygrid,
xgrid,
y_bnd,
x_bnd,
ny,
nx,
num_sources_y=num_sources_y,
num_sources_x=num_sources_x) * contributionprofiles[tile_idx]
pass
if do_singlestep_bool:
(ss_inversioncoeffs_list,
ss_errs_list, ss_tikparams_list) = inversionevalfunc(
OpenCL_CTX, ss_rowselects, ss_inversions, ss_inversionsfull,
ss_inverses, ss_nresults, inputmats_holesfilled, tikparam)
ss_fullinverse = np.zeros(
(len(reflectors) + 1, wfmdict[channel].data.shape[1] //
xydownsample, wfmdict[channel].data.shape[0] // xydownsample),
dtype='d')
for tile_idx in range(len(minyminx_corners)):
(yidx, xidx) = minyminx_corners[tile_idx]
ss_fullinverse[:, yidx:(yidx + ny), xidx:(
xidx + nx)] += greensinversion.buildconcreteinverse(
ss_inversioncoeffs_list[tile_idx], reflectors, ygrid,
xgrid, y_bnd, x_bnd, ny, nx, num_sources_y,
num_sources_x) * contributionprofiles[tile_idx]
pass
# for tile_idx in range(len(minyminx_corners)):
# (yidx,xidx)=minyminx_corners[tile_idx]
# #
# (ss_inversioncoeffs,ss_residual,errs,ss_tikparams)=greensinversion.performinversionsteps(ss_rowselects,ss_inversions,ss_inversionsfull,ss_inverses,ss_nresults,wfmdict[channel].data[(xidx*xydownsample):((xidx+nx)*xydownsample):xydownsample,(yidx*xydownsample):((yidx+ny)*xydownsample):xydownsample,startframe:endframe].transpose((2,1,0)),tikparam) # transpose to convert dataguzzler axis ordering (x,y,t) to greensinversion ordering (t,y,x)
# #
# ss_concreteinverse=greensinversion.buildconcreteinverse(ss_inversioncoeffs,reflectors,ygrid,xgrid,y_bnd,x_bnd,ny,nx)
# # concreteinverse is (len(reflectors)+1,ny,nx)... first layer is surface
# # ... accumulate contributions of each tile to full inverse
# ss_fullinverse[:,yidx:(yidx+ny),xidx:(xidx+nx)] += ss_concreteinverse*contributionprofiles[tile_idx]
# pass
pass
if tikparam is None:
outpng_fname = "%s_greensinversion.png" % (inputfile_basename)
movieoutdirname = "%s_greensinversion_movie/" % (inputfile_basename)
movieoutfilename = "%s_greensinversion_movie_depth_%%05.2f.png" % (
inputfile_basename)
outpngflat_fname = "%s_greensinversionflat.png" % (inputfile_basename)
movieoutflatfilename = "%s_greensinversionflat_movie_depth_%%05.2f.png" % (
inputfile_basename)
pass
else:
outpng_fname = "%s_greensinversion_tik_%g.png" % (inputfile_basename,
tikparam)
movieoutdirname = "%s_greensinversion_tik_%g_movie/" % (
inputfile_basename, tikparam)
movieoutfilename = "%s_greensinversion_tik_%g_movie_depth_%%05.2f.png" % (
inputfile_basename, tikparam)
outpngflat_fname = "%s_greensinversionflat_tik_%g.png" % (
inputfile_basename, tikparam)
movieoutflatfilename = "%s_greensinversionflat_tik_%g_movie_depth_%%05.2f.png" % (
inputfile_basename, tikparam)
pass
(fig, subplots, images) = greensinversion.plotconcreteinverse(
nextfignum, dc_numplotrows_int, dc_numplotcols_int, saturation_map,
fullinverse, reflectors, -10000.0, 30000.0, fullinverse_y_bnd,
fullinverse_x_bnd, num_sources_y, num_sources_x)
nextfignum += 1
outpng_href = dc_value.hrefvalue(quote(outpng_fname), _dest_href)
fig.savefig(outpng_href.getpath())
reslist.append((("dc:greensinversion_figure", {
"tikparam": str(tikparam)
}), outpng_href))
(fig, subplots, images) = greensinversion.plotconcreteinverse(
nextfignum, dc_numplotrows_int, dc_numplotcols_int, saturation_map,
flatfullinverse, reflectors, -10000.0, 30000.0, fullinverse_y_bnd,
fullinverse_x_bnd, num_sources_y, num_sources_x)
nextfignum += 1
outpngflat_href = dc_value.hrefvalue(quote(outpngflat_fname), _dest_href)
fig.savefig(outpngflat_href.getpath())
reslist.append((("dc:greensinversion_figure", {
"tikparam": str(tikparam)
}), outpngflat_href))
movieoutdirhref = dc_value.hrefvalue(quote(movieoutdirname),
contexthref=_dest_href)
(nextfignum, plots, images, plothrefs,
depths) = greensinversion.inversion.plotconcreteinversemovie(
nextfignum,
movieoutdirhref,
movieoutfilename,
saturation_map,
fullinverse,
reflectors,
-10000.0,
30000.0,
fullinverse_y_bnd,
fullinverse_x_bnd,
num_sources_y,
num_sources_x,
dpi=300)
for cnt in range(len(plothrefs)):
reslist.append((("dc:greensinversion_movie_frame", {
"tikparam": str(tikparam),
"depth": str(depths[cnt])
}), plothrefs[cnt]))
pass
(nextfignum, plots, images, plotflathrefs,
depths) = greensinversion.inversion.plotconcreteinversemovie(
nextfignum,
movieoutdirhref,
movieoutflatfilename,
saturation_map,
flatfullinverse,
reflectors,
-10000.0,
30000.0,
fullinverse_y_bnd,
fullinverse_x_bnd,
num_sources_y,
num_sources_x,
dpi=300)
for cnt in range(len(plotflathrefs)):
reslist.append((("dc:greensinversionflat_movie_frame", {
"tikparam": str(tikparam),
"depth": str(depths[cnt])
}), plotflathrefs[cnt]))
pass
if do_singlestep_bool:
(ss_fig, ss_subplots, ss_images) = greensinversion.plotconcreteinverse(
nextfignum, dc_numplotrows_int, dc_numplotcols_int, saturation_map,
ss_fullinverse, reflectors, -10000.0, 30000.0, fullinverse_y_bnd,
fullinverse_x_bnd, num_sources_y, num_sources_x)
nextfignum += 1
if tikparam is None:
ss_outpng_fname = "%s_ss_greensinversion.png" % (
inputfile_basename)
ss_movieoutdirname = "%s_ss_greensinversion_movie/" % (
inputfile_basename)
ss_movieoutfilename = "%s_ss_greensinversion_movie_depth_%%05.2f.png" % (
inputfile_basename)
pass
else:
ss_outpng_fname = "%s_ss_greensinversion_tik_%g.png" % (
inputfile_basename, tikparam)
ss_movieoutdirname = "%s_ss_greensinversion_tik_%g_movie/" % (
inputfile_basename, tikparam)
ss_movieoutfilename = "%s_ss_greensinversion_tik_%g_movie_depth_%%05.2f.png" % (
inputfile_basename, tikparam)
pass
ss_outpng_href = dc_value.hrefvalue(quote(ss_outpng_fname), _dest_href)
ss_fig.savefig(ss_outpng_href.getpath())
reslist.append((("dc:greensinversion_singlestep_figure", {
"tikparam": str(tikparam)
}), ss_outpng_href))
ss_movieoutdirhref = dc_value.hrefvalue(quote(ss_movieoutdirname),
contexthref=_dest_href)
(nextfignum, ss_plots, ss_images, ss_plothrefs,
ss_depths) = greensinversion.inversion.plotconcreteinversemovie(
nextfignum,
ss_movieoutdirhref,
ss_movieoutfilename,
saturation_map,
ss_fullinverse,
reflectors,
-10000.0,
30000.0,
fullinverse_y_bnd,
fullinverse_x_bnd,
num_sources_y,
num_sources_x,
resolution=300)
for cnt in range(len(ss_plothrefs)):
reslist.append((("dc:ss_greensinversion_movie_frame", {
"tikparam": str(tikparam),
"depth": str(ss_depths[cnt])
}), ss_plothrefs[cnt]))
pass
pass
outwfmdict = {}
outwfmdict[dc_cadmodel_channel_str] = copy.deepcopy(
wfmdict[dc_cadmodel_channel_str])
SplitTextureChans = dgm.GetMetaDatumWIStr(wfmdict[dc_cadmodel_channel_str],
"TextureChans", "").split("|")
PrefixedTextureChans = "|".join([
dc_prefix_str + TexChanPrefix + TexChan
for TexChan in SplitTextureChans
])
PrefixedFlatTextureChans = "|".join([
dc_prefix_str + "_flat" + TexChanPrefix + TexChan
for TexChan in SplitTextureChans
])
gi_3d = dg.wfminfo()
#gi_3d.Name=dc_prefix_str+dc_cadmodel_channel_str
gi_3d.Name = "Proj" + dc_prefix_str + TexChanPrefix + dc_cadmodel_channel_str
gi_3d.dimlen = np.array((1, ), dtype='i8')
gi_3d.data = np.array((1, ), dtype='f')
dgm.AddMetaDatumWI(gi_3d,
dgm.MetaDatum("VRML97GeomRef", dc_cadmodel_channel_str))
dgm.AddMetaDatumWI(gi_3d,
dgm.MetaDatum("X3DGeomRef", dc_cadmodel_channel_str))
#texchanprefix=gi_3d.Name[:gi_3d.Name.find(dc_unprefixed_texname_str)]
dgm.AddMetaDatumWI(
gi_3d, dgm.MetaDatum("TexChanPrefix", dc_prefix_str + TexChanPrefix))
dgm.AddMetaDatumWI(gi_3d,
dgm.MetaDatum("TextureChans", PrefixedTextureChans))
outwfmdict[gi_3d.Name] = gi_3d
giflat_3d = dg.wfminfo()
#gi_3d.Name=dc_prefix_str+dc_cadmodel_channel_str
giflat_3d.Name = "Proj" + dc_prefix_str + "flat_" + TexChanPrefix + dc_cadmodel_channel_str
giflat_3d.dimlen = np.array((1, ), dtype='i8')
giflat_3d.data = np.array((1, ), dtype='f')
dgm.AddMetaDatumWI(giflat_3d,
dgm.MetaDatum("VRML97GeomRef", dc_cadmodel_channel_str))
dgm.AddMetaDatumWI(giflat_3d,
dgm.MetaDatum("X3DGeomRef", dc_cadmodel_channel_str))
#texchanprefix=giflat_3d.Name[:gi_3d.Name.find(dc_unprefixed_texname_str)]
dgm.AddMetaDatumWI(
giflat_3d,
dgm.MetaDatum("TexChanPrefix",
dc_prefix_str + "_flat" + TexChanPrefix))
dgm.AddMetaDatumWI(giflat_3d,
dgm.MetaDatum("TextureChans", PrefixedFlatTextureChans))
outwfmdict[giflat_3d.Name] = giflat_3d
#outwfm_flat.Name=dc_prefix_str+dc_inversion_channel_str+"flat"
outwfm = dg.wfminfo()
#outwfm.Name="greensinversion"
outwfm.Name = dc_prefix_str + dc_inversion_channel_str
outwfmdict[outwfm.Name] = outwfm
outwfm_flat = dg.wfminfo()
#outwfm.Name="greensinversion"
outwfm_flat.Name = dc_prefix_str + "_flat" + dc_inversion_channel_str
outwfmdict[outwfm_flat.Name] = outwfm_flat
# Shift IniVals according to xydownsample:
# IniVal[0] is X coordinate of center of corner pixel of undownsampled image
# IniVal[0] is X coordinate of center of corner pixel downsampled image
# but that pixel is twice as big, so the corner of the image itself
# has changed!
dgm.AddMetaDatumWI(outwfm, dgm.MetaDatum("IniVal1", IniVal[0]))
dgm.AddMetaDatumWI(outwfm, dgm.MetaDatum("IniVal2", IniVal[1]))
dgm.AddMetaDatumWI(outwfm,
dgm.MetaDatum("Step1", XStepMeters * xydownsample))
dgm.AddMetaDatumWI(outwfm,
dgm.MetaDatum("Step2", YStepMeters * xydownsample))
dgm.AddMetaDatumWI(outwfm, dgm.MetaDatum("Coord1", Coord[0]))
dgm.AddMetaDatumWI(outwfm, dgm.MetaDatum("Coord2", Coord[1]))
dgm.AddMetaDatumWI(outwfm, dgm.MetaDatum("Units1", Units[0]))
dgm.AddMetaDatumWI(outwfm, dgm.MetaDatum("Units2", Units[1]))
dgm.AddMetaDatumWI(outwfm, dgm.MetaDatum("IniVal3", 0.0))
dgm.AddMetaDatumWI(outwfm, dgm.MetaDatum("Step3", 1.0))
dgm.AddMetaDatumWI(outwfm, dgm.MetaDatum("Coord3", "Depth Index"))
dgm.AddMetaDatumWI(outwfm, dgm.MetaDatum("Units3", "unitless"))
dgm.AddMetaDatumWI(outwfm, dgm.MetaDatum("AmplCoord", "Heating intensity"))
dgm.AddMetaDatumWI(outwfm, dgm.MetaDatum("AmplUnits", "J/m^2"))
# Copy landmark metadata
LandmarkMD = [
MDName for MDName in list(wfmdict[channel].MetaData.keys())
if MDName.startswith("LANDMARK_")
]
for LandmarkName in LandmarkMD:
dgm.AddMetaDatumWI(
outwfm, copy.deepcopy(wfmdict[channel].MetaData[LandmarkName]))
pass
outwfm_flat.MetaData = copy.deepcopy(outwfm.MetaData)
if channel_weights is not None:
#outwfm_weights=copy.deepcopy(wfmdict[channel_weights])#dg.wfminfo()
#outwfm_weights.Name="greensinversion_weights"
outwfm_weights = dg.wfminfo()
outwfm_weights.Name = dc_prefix_str + dc_inversion_channel_str + "_weights"
outwfm_weights.data = wfmdict[
channel_weights].data[::xydownsample, ::xydownsample]
outwfm_weights.dimlen = np.array(outwfm_weights.data.shape)
outwfm_weights.ndim = 2
outwfm_weights.MetaData = copy.deepcopy(outwfm.MetaData)
dgm.AddMetaDatumWI(outwfm_weights,
dgm.MetaDatum("AmplCoord", "Weighting"))
dgm.AddMetaDatumWI(outwfm_weights,
dgm.MetaDatum("AmplUnits", "Unitless"))
outwfmdict[outwfm_weights.Name] = outwfm_weights
pass
if do_singlestep_bool:
ss_outwfm = copy.deepcopy(outwfm)
ss_outwfm.Name = "ss_greensinversion"
outwfmdict[ss_outwfm.Name] = ss_outwfm
pass
# dgs file is written in (X,Y,Z) fortran order, so we write
# dimlen in reverse order and transpose the data
outwfm.ndim = 3
outwfm.dimlen = np.array(fullinverse.shape[::-1])
outwfm.data = fullinverse.transpose().astype(np.float32)
outwfm.NeedData = False
outwfm.NeedMetaData = False
outwfm.HaveData = True
outwfm.HaveMetaData = True
outwfm_flat.ndim = 3
outwfm_flat.dimlen = np.array(flatfullinverse.shape[::-1])
outwfm_flat.data = flatfullinverse.transpose().astype(np.float32)
outwfm_flat.NeedData = False
outwfm_flat.NeedMetaData = False
outwfm_flat.HaveData = True
outwfm_flat.HaveMetaData = True
outwfm_saturationmap = dg.wfminfo()
outwfm_saturationmap.Name = "saturation_map"
outwfmdict[outwfm_saturationmap.Name] = outwfm_saturationmap
outwfm_saturationmap.dimlen = np.array(saturation_map.shape[::-1])
outwfm_saturationmap.data = saturation_map.transpose().astype(np.float32)
outwfm_saturationmap.ndim = outwfm_saturationmap.dimlen.shape[0]
outwfm_saturationmap.NeedData = False
outwfm_saturationmap.NeedMetaData = False
outwfm_saturationmap.HaveData = True
outwfm_saturationmap.HaveMetaData = True
outwfm_saturationmap.MetaData = copy.deepcopy(outwfm.MetaData)
if do_singlestep_bool:
ss_outwfm.ndim = 3
ss_outwfm.dimlen = np.array(ss_fullinverse.shape[::-1])
ss_outwfm.data = ss_fullinverse.transpose().astype(np.float32)
ss_outwfm.NeedData = False
ss_outwfm.NeedMetaData = False
ss_outwfm.HaveData = True
ss_outwfm.HaveMetaData = True
pass
if tikparam is None:
outdgs_fname = "%s_greensinversion.dgs" % (inputfile_basename)
pass
else:
outdgs_fname = "%s_greensinversion_tik_%g.dgs" % (inputfile_basename,
tikparam)
pass
outdgs_href = dc_value.hrefvalue(quote(outdgs_fname), _dest_href)
dgf.savesnapshot(outdgs_href.getpath(), outwfmdict)
reslist.append((("dc:greensinversion_dgsfile", {
"tikparam": str(tikparam)
}), outdgs_href))
if do_singlestep_bool:
pass
#
# greensconvolution_params.get_opencl_context()
# tile_idx=14
# (yidx,xidx)=minyminx_corners[tile_idx]
#
# inputmats=[wfmdict[channel].data[(xidx*xydownsample):((xidx+nx)*xydownsample):xydownsample,(yidx*xydownsample):((yidx+ny)*xydownsample):xydownsample,startframe:endframe].transpose((2,1,0))]
# greeninversion.inversion.parallelperforminversionsteps(greensconvolution_params.OpenCL_CTX,rowselects,inversions,inversionsfull,inverses,nresults,inputmats,None)
return reslist