def model_objects_galfit(dataimage,galfitparamfile,show_residualimg=False,verbose=True):
"""
~ ~ ~ ~ STILL UNDER CONSTRUCTION/TESTING ~ ~ ~ ~
Function template for modeling of objects with galfit
--- INPUT ---
dataimage
galfitparamfile
show_residualimg
verbose
--- EXAMPLE OF USE ---
import tdose_model_FoV as tmf
"""
print(' # # # # # # # # # model_objects_galfit() still under development/testing # # # # # # # # #')
if verbose: print(' - Use GALFIT model output to obtain model parameters for residual between (GALFIT) model and data')
if verbose: print(' ----------- Started on '+tu.get_now_string()+' ----------- ')
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
galfitparams = tu.galfit_loadoutput(galfitparamfile)
Nmodels = len(galfitparams)
imgsize = dataimage.shape
xgrid, ygrid = tu.gen_gridcomponents(imgsize)
for oo, obj in enumerate(Nmodels):
xxx = yyy
output = param_optimized, param_cov
image = '/Volumes/DATABCKUP3/MUSE/candels-cdfs-02/acs_814w_candels-cdfs-02_cut_v1.0.fits'
sexcatalog = '/Volumes/DATABCKUP3/MUSE/candels-cdfs-02/catalog_photometry_candels-cdfs-02.fits'
fileout = '/Volumes/DATABCKUP3/MUSE/candels-cdfs-02/galfit_inputfile_acs_814w_candels-cdfs-02-sextractor.txt'
tu.galfit_buildinput_fromssextractoroutput(fileout,sexcatalog,image,objecttype='gaussian',verbose=verbose)
galfitoutput = tu.galfit_run(fileout,verbose=verbose)
param_optimized = tu.galfit_results2paramlist(galfitoutput,verbose=verbose)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if verbose: print(' ----------- Finished on '+tu.get_now_string()+' ----------- ')
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if show_residualimg:
if verbose: print(' - Displaying the residual image between data image and galfit model (assuming gaussians)')
galfit_img = tmf.modelimage_multigauss((xgrid,ygrid), output[0] , showmodelimg=False)
res_img = dataimage-galfit_img
plt.imshow(res_img,interpolation='none', vmin=1e-5, vmax=np.max(res_img), norm=mpl.colors.LogNorm())
plt.title('Data Residual = Data Image - Galfit Model Image ')
plt.show()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
return output
def remove_object(datacube,sourcemodelcube,objects=[1,3],remove=True,dataext=1,sourcemodelext=1,
savecube=False,savedir=None,clobber=False,verbose=True):
"""
Use source model cube to remove object(s) from data cube
--- INPUT ---
datacube Datacube to modify
sourcemodelcube Source model cube of data cube defining models of each source in the datacube
objects The objects to remove from data cube. Provide number of source sub-cube in
"sourcemodelcube".
remove If true objects in "objects" will be removed from "datacube". If remove=False
everything but the objects listed in "objects" will be removed.
dataext Extension of datacube containing actual data
sourcemodelext Extension of source model cube containing the models
savecube If a string is provided the modified cube will be stored in a new fits file
appending the provided string to the data cube file name.
savedir If a directory path is provided, the modified cube will be stored here. Otherwise
it will be stored at the same location as the datacube.
clobber If true any existing fits file will be overwritten if modified cube is saved
verbose Toggle verbosity
--- EXAMPLE OF USE ---
import tdose_modify_cube
datacube = 'datacube_spectra_are_extracted_from.fits'
sourcemodelcube = 'tdose_source_modelcube.fits'
modified_cube = tdose_modify_cube.remove_object(datacube,sourcemodelcube,savecube='source1and3removed')
"""
if verbose: print(' - Loading data and source model cubes ')
datacubehdu = fits.open(datacube)
dataarr = datacubehdu[dataext].data
dataarr_hdr = datacubehdu[dataext].header
sourcemodelhdu = fits.open(sourcemodelcube)
sourcemodel = sourcemodelhdu[sourcemodelext].data
sourcemodel_hdr = sourcemodelhdu[sourcemodelext].header
sourcemodelhdu.close()
Nmodels = sourcemodel.shape[0]
models = np.arange(Nmodels)
if verbose: print(' - Check that all objects indicated are present in source model cube')
objects = np.asarray(objects)
maxobj = np.max(np.abs(objects))
if maxobj >= Nmodels:
sys.exit(' ---> Object model "'+str(maxobj)+'" is not included in source model cube (models start at 0) ')
else:
if verbose: print((' All object models appear to be included in the '+str(Nmodels)+' source models found in cube'))
if verbose: print(' - Determining objects (source models) to remove from data cube ')
if remove:
obj_remove = objects
obj_keep = np.setdiff1d(models,obj_remove)
else:
obj_keep = objects
obj_remove = np.setdiff1d(models,obj_keep)
remove_cube = np.sum(sourcemodel[obj_remove.astype(int),:,:,:],axis=0)
modified_cube = dataarr - remove_cube
if savecube:
datacubehdu[dataext].data = modified_cube # Replacing original data with modified cube
if savedir is None:
outname = datacube.replace('.fits','_'+str(savecube)+'.fits')
else:
outname = savedir+datacube.split('/')[-1].replace('.fits','_'+str(savecube)+'.fits')
if verbose: print((' - Saving modified cube to \n '+outname))
if verbose: print(' (Using datacube header with modification keywords appended) ')
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# hducube = fits.PrimaryHDU(modified_cube) # default HDU with default minimal header
# hducube.header = dataarr_hdr
# adding hdrkeys: '---KEY--', '----------------MAX LENGTH COMMENT-------------'
dataarr_hdr.append(('MODIFIED', 'True','Cube is modified with tdose_modify_cube.py?'),end=True)
dataarr_hdr.append(('MODTIME ', tu.get_now_string(),'Date and time of cube modification'),end=True)
dataarr_hdr.append(('SMC ', sourcemodelcube,'Name of Source Model Cube'),end=True)
dataarr_hdr.append(('NREMOVE ', len(obj_remove),'Number of sources removed from cube'),end=True)
dataarr_hdr.append(('NKEEP ', len(obj_keep),'Number of sources kept in cube'),end=True)
dataarr_hdr.append(('SREMOVE ',','.join([str(oo) for oo in obj_remove])),'Source indexes removed',end=True)
dataarr_hdr.append(('SKEEP ',','.join([str(oo) for oo in obj_keep])),'Source indexes kept',end=True)
# dataarr_hdr.append(('COMMENT ','Source indexes removed:'+','.join([str(oo) for oo in obj_remove])),end=True)
# dataarr_hdr.append(('COMMENT ','Source indexes kept:'+','.join([str(oo) for oo in obj_keep])),end=True)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
datacubehdu.writeto(outname,overwrite=clobber)
return modified_cube
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
def perform_modification(setupfile='./tdose_setup_template_modify.txt',clobber=False,verbose=True):
"""
Modify a data cube using the information stored in a generated TDOSE source model cube.
--- INPUT ---
setupfile TDOSE modification setup file. Template can be generated
with tdose_utilities.generate_setup_template_modify()
clobber Overwrite existing output?
verbose Toggle verbosity
--- EXAMPLE OF USE ---
import tdose_modify_cube as tmc
setupfile = './tdose_setup_template_modify.txt'
tmc.perform_modification(setupfile=setupfile)
"""
start_time = time.clock()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if verbose: print('==================================================================================================')
if verbose: print(' TDOSE: Loading setup '+\
' ( Total runtime = '+str("%10.4f" % (time.clock() - start_time))+' seconds )')
setupdic = tu.load_setup(setupfile,verbose=verbose)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if verbose: print('==================================================================================================')
if verbose: print(' TDOSE: Logging setup '+\
' ( Total runtime = '+str("%10.4f" % (time.clock() - start_time))+' seconds )')
setuplog = setupdic['modified_cube_dir']+setupfile.split('/')[-1].replace('.txt','_logged.txt')
if os.path.isfile(setuplog) & (clobber == False):
if verbose: print(' - WARNING Logged setupfile exists and clobber = False. Not storing setup ')
else:
if verbose: print(' - Writing setup and command to spec1D_directory to log extraction setup and command that was run')
setupinfo = open(setupfile,'r')
setupcontent = setupinfo.read()
setupinfo.close()
cmdthatwasrun = "import tdose_modify_cube as tmc; tmc.perform_modification(setupfile='%s',clobber=%s,verbose=%s)" % \
(setuplog,clobber,verbose)
loginfo = open(setuplog, 'w')
loginfo.write("# The setup file appended below was run with the command: \n# "+cmdthatwasrun+
" \n# on "+tu.get_now_string()+'\n# ')
loginfo.write(setupcontent)
loginfo.close()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if verbose: print('==================================================================================================')
if verbose: print(' TDOSE: Modifying data cube '+\
' ( Total runtime = '+str("%10.4f" % (time.clock() - start_time))+' seconds )')
savestring = 'satelitesremoved'
if setupdic['sources_action'].lower() == 'remove':
removing = True
elif setupdic['sources_action'].lower() == 'keep':
removing = False
else:
if verbose: print(' - WARNING "sources_action" keyword in setup ('+setupdic['sources_action']+') is invalid. Removing source.')
removing = True
modified_cube = tmc.remove_object(setupdic['data_cube'],setupdic['source_model_cube'],
objects=setupdic['modify_sources_list'],remove=removing,
dataext=setupdic['cube_extension'],
sourcemodelext=setupdic['source_extension'],savecube=setupdic['modified_cube'],
savedir=setupdic['modified_cube_dir'],clobber=clobber,verbose=verbose)
def model_objects_gauss(param_init,dataimage,optimizer='curve_fit',max_centroid_shift=None,
datanoise=None,show_residualimg=True,verbose=True):
"""
Optimize residual between model (multiple Gaussians) and data with least squares in 2D
--- INPUT ---
param_init Initial guess on parameters (defines the number of gaussians to fit for)
dataimage Image to model with multiple Gaussians
optimizer Chose the optimizer to use
leastsq scipy.optimize.leastsq(); not ideal for 2D images...
Tries to optimize the residual function
curve_fit scipy.optimize.curve_fit()
Tries to optimize using the model function
max_centroid_shift Maximum offset in pixels of (x,y) centroid position of sources when modeling
I.e. impose ypix_centroid +/- max_centroid_shift and xpix_centroid +/- max_centroid_shift
bounds om parameters when fitting.
datanoise Image of sigmas, i.e., sqrt(variance) to use as weights when optimizing fit
using curve_fit
verbose Toggle verbosity
--- EXAMPLE OF USE ---
import tdose_model_FoV as tmf
param_init = [18,31,1*0.3,2.1*0.3,1.2*0.3,30*0.3, 110,90,200*0.5,20.1*0.5,15.2*0.5,0*0.5]
dataimg = afits.open('/Users/kschmidt/work/TDOSE/mock_cube_sourcecat161213_tdose_mock_cube.fits')[0].data[0,:,:]
param_optimized, param_cov = tmf.model_objects_gauss(param_init,dataimg,verbose=True)
"""
if verbose: print(' - Optimize residual between model (multiple Gaussians) and data with the optimizer "'+optimizer+'"')
if verbose: print(' ----------- Started on '+tu.get_now_string()+' ----------- ')
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if optimizer == 'leastsq':
best_param, cov, info, message, succesflag = opt.leastsq(tmf.residual_multigauss,param_init, args=(dataimage),
full_output=True)
output = best_param, cov, info, message, succesflag
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
elif optimizer == 'curve_fit':
imgsize = dataimage.shape
xgrid, ygrid = tu.gen_gridcomponents(imgsize)
if datanoise is not None:
sigma = datanoise.ravel()
else:
sigma = datanoise
maxfctcalls = 30000
Nsources = int(len(param_init)/6.)
# [yposition,xposition,fluxscale,sigmay ,sigmax ,angle]
param_bounds = ([0 ,0 ,0 ,1./2.355,1./2.355,-360 ]*Nsources,
[np.inf , np.inf ,np.inf , np.inf , np.inf ,360 ]*Nsources)
Nnonfinite = len(dataimage[np.where(~np.isfinite(dataimage))])
if Nnonfinite > 0:
dataimage[np.where(~np.isfinite(dataimage))] = 0.0
if verbose: print((' WARNING: '+str(Nnonfinite)+' Pixels in dataimage that are not finite; '
'setting them to 0 to prevent curve_fit crash'))
if max_centroid_shift is not None:
init_y = param_init[0::6]
init_x = param_init[1::6]
bound_xlow = init_x-max_centroid_shift
bound_xhigh = init_x+max_centroid_shift
bound_ylow = init_y-max_centroid_shift
bound_yhigh = init_y+max_centroid_shift
param_bounds[0][0::6] = bound_ylow
param_bounds[1][0::6] = bound_yhigh
param_bounds[0][1::6] = bound_xlow
param_bounds[1][1::6] = bound_xhigh
try:
param_optimized, param_cov = opt.curve_fit(tmf.curve_fit_function_wrapper, (xgrid, ygrid),dataimage.ravel(),
p0 = param_init, sigma=sigma,maxfev=maxfctcalls, bounds=param_bounds)
output = param_optimized, param_cov
except:
print(' WARNING: Curve_fit failed so returning param_init [ypos,xpos,fluxscale,sigmay,sigmax,angle]: ')
print((' '+str(param_init)))
print(' i.e. the intiial guess of the parameters')
print((' Likely using "maximum function call" of '+str(maxfctcalls)+')'))
output = param_init, None
#pdb.set_trace()
else:
sys.exit(' ---> Invalid optimizer ('+optimizer+') chosen in model_objects_gauss()')
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if verbose: print('\n ----------- Finished on '+tu.get_now_string()+' ----------- ')
#if verbose: print ' - The returned best-fit parameters are \n ',output[0]
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if show_residualimg:
if verbose: print(' - Displaying the residual image between initial guess and optimized parameters')
init_img = tmf.modelimage_multigauss((xgrid,ygrid), param_init , showmodelimg=False)
best_img = tmf.modelimage_multigauss((xgrid,ygrid), output[0] , showmodelimg=False)
res_img = init_img-best_img
plt.imshow(res_img,interpolation='none', vmin=1e-5, vmax=np.max(res_img), norm=mpl.colors.LogNorm())
plt.title('Model Residual = Initial Parameter Image - Optimized Parameter Image')
plt.show()
res_img = best_img-dataimage
plt.imshow(res_img,interpolation='none', vmin=1e-5, vmax=np.max(res_img), norm=mpl.colors.LogNorm())
plt.title('Data Residual = Optimized Parameter Image - Data Image')
plt.show()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
return output