Tomopy Exception

In [1]: runfile('/run/media/mwolf/WOLF_DATA/txm-2016-06-07-aps-32-ID-C/Scripts/rec_ASTRA_one.py', wdir='/run/media/mwolf/WOLF_DATA/txm-2016-06-07-aps-32-ID-C')

#### Processing NCA2b_3.9V_8400eV_10X_stepscan_721proj_1s_wfilter_5_123.h5
Test reconstruction of slice [0]
/usr/lib/python2.7/site-packages/dxchange-0.1.1-py2.7.egg/dxchange/exchange.py:644: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.
Traceback (most recent call last):

  File "<ipython-input-1-283c6f023cda>", line 1, in <module>
    runfile('/run/media/mwolf/WOLF_DATA/txm-2016-06-07-aps-32-ID-C/Scripts/rec_ASTRA_one.py', wdir='/run/media/mwolf/WOLF_DATA/txm-2016-06-07-aps-32-ID-C')

  File "/usr/lib/python2.7/site-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 714, in runfile
    execfile(filename, namespace)

  File "/usr/lib/python2.7/site-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 81, in execfile
    builtins.execfile(filename, *where)

  File "/run/media/mwolf/WOLF_DATA/txm-2016-06-07-aps-32-ID-C/Scripts/rec_ASTRA_one.py", line 158, in <module>
    if reconstruction_test: rec_test(file_name, sino_start, sino_end, astra_method, extra_options, num_iter)

  File "/run/media/mwolf/WOLF_DATA/txm-2016-06-07-aps-32-ID-C/Scripts/rec_ASTRA_one.py", line 74, in rec_test
    rec = tomopy.recon(prj, theta, center=best_center/pow(2,level), algorithm=tomopy.astra, options={'proj_type':proj_type,'method':astra_method,'extra_options':extra_options,'num_iter':num_iter}, emission=False)

  File "/usr/lib/python2.7/site-packages/tomopy-1.0.0-py2.7-linux-x86_64.egg/tomopy/recon/algorithm.py", line 284, in recon
    tomo, center_arr, recon, _get_func(algorithm), args, kwargs, ncore, nchunk)

  File "/usr/lib/python2.7/site-packages/tomopy-1.0.0-py2.7-linux-x86_64.egg/tomopy/recon/algorithm.py", line 353, in _dist_recon
    nchunk=nchunk)

  File "/usr/lib/python2.7/site-packages/tomopy-1.0.0-py2.7-linux-x86_64.egg/tomopy/util/mproc.py", line 222, in distribute_jobs
    clear_queue(queue, shared_arrays, shared_out)

  File "/usr/lib/python2.7/site-packages/tomopy-1.0.0-py2.7-linux-x86_64.egg/tomopy/util/mproc.py", line 278, in clear_queue
    _arg_parser(params)

  File "/usr/lib/python2.7/site-packages/tomopy-1.0.0-py2.7-linux-x86_64.egg/tomopy/util/mproc.py", line 261, in _arg_parser
    result = func(*func_args, **kwargs)

  File "/usr/lib/python2.7/site-packages/tomopy-1.0.0-py2.7-linux-x86_64.egg/tomopy/recon/wrappers.py", line 122, in astra
    astra_run(*args, **kwargs)

  File "/usr/lib/python2.7/site-packages/tomopy-1.0.0-py2.7-linux-x86_64.egg/tomopy/recon/wrappers.py", line 206, in astra_run
    opts['proj_type'], proj_geom, vol_geom)

  File "/usr/lib/python2.7/site-packages/astra/creators.py", line 558, in create_projector
    return projector.create(cfg)

  File "/usr/lib/python2.7/site-packages/astra/projector.py", line 36, in create
    return p.create(config)

  File "astra/projector_c.pyx", line 63, in astra.projector_c.create (astra/projector_c.cpp:1021)

Exception: Error creating projector.

Tomopy Script

import tomopy
import dxchange
import astra
import sirtfbp
import time

proj_type='cuda'


##################################### Inputs #########################################################################
file_name = '/local/prom04/2016-06/Cabana-Jimenez/NMC1a_pristine_8400eV_10X_step_scan_721proj_2s_0_119.h5' # best center = 1190 # Remove first dark
output_name = '/local/prom04/2016-06/Cabana-Jimenez/NMC1a_pristine_8400eV_10X_step_scan_721proj_2s_0_119_sirtfbp_recon/NMC1a_pristine_8400eV_10X_step_scan_721proj_2s_0_119_sirtfbp_recon_'

file_name = '/local/prom04/2016-06/Cabana-Jimenez/NCA2b_3.9V_8400eV_10X_stepscan_721proj_1s_wfilter_5_123.h5' # best center = 1166
output_name = '/local/prom04/2016-06/Cabana-Jimenez/NCA2b_3.9V_8400eV_10X_stepscan_721proj_1s_wfilter_5_123_sirtfbp_recon/9V_8400eV_10X_stepscan_721proj_1s_wfilter_5_123_sirtfbp_recon_'

file_name = '/local/prom04/2016-06/Cabana-Jimenez/lmo_pristine_XANEStomo_21_6576eV_184.h5' # best center =1206
#output_name = '/local/prom04/2016-06/Cabana-Jimenez/lmo_pristine_XANEStomo_21_6576eV_184_fbp_parzen_recon/lmo_pristine_XANEStomo_21_6576eV_184_sirtfbp_recon_'
output_name = '/local/prom04/2016-06/Cabana-Jimenez/lmo_pristine_XANEStomo_21_6576eV_184_sirtfbp_recon/lmo_pristine_XANEStomo_21_6576eV_184_sirtfbp_recon_'

file_name = 'NCA2b_3.9V_8400eV_10X_stepscan_721proj_1s_wfilter_5_123.h5' # best center =1206
output_name = 'NCA2b_3.9V_8400eV_10X_stepscan_recon/NCA2b_3.9V_8400eV_10X_stepscan_recon'

reconstruction_test = True
best_center = 1264
sino_start = 0
sino_end = 2048
miss_angles = [0,721]
level = 3
medfilt_size = 2
######################################################################################################################






def rec_test(file_name, sino_start, sino_end, astra_method, extra_options, num_iter=1):

    print '\n#### Processing '+ file_name
    print "Test reconstruction of slice [%d]" % sino_start
    # Read HDF5 file.
    prj, flat, dark, theta = dxchange.read_aps_32id(file_name, sino=(sino_start, sino_end))
    # Manage the missing angles:
    if theta is None:
        theta  = tomopy.angles(prj.shape[0])
    else:
        pass
#    prj = np.concatenate((prj[0:miss_angles[0],:,:], prj[miss_angles[1]+1:-1,:,:]), axis=0)
#    theta = np.concatenate((theta[0:miss_angles[0]], theta[miss_angles[1]+1:-1]))
    prj = prj[miss_angles[0]:miss_angles[1],:,:]
    dark = dark[1:,:,:]
    theta = theta[miss_angles[0]:miss_angles[1]]

    # normalize the prj
    prj = tomopy.normalize(prj, flat, dark)

    #prj[prj<=0]=0.1
    #prj = tomopy.minus_log(prj)
    
    # import pdb; pdb.set_trace()
    # remove ring artefacts
    prj = tomopy.remove_stripe_ti(prj)
    prj = tomopy.remove_stripe_sf(prj,10)
    
    # Median filter:
    if medfilt_size:
        prj = tomopy.median_filter(prj,size=medfilt_size)

    if level>0:
        prj = tomopy.downsample(prj, level=level)

    # reconstruct 
    rec = tomopy.recon(prj, theta, center=best_center/pow(2,level), algorithm=tomopy.astra, options={'proj_type':proj_type,'method':astra_method,'extra_options':extra_options,'num_iter':num_iter}, emission=False)
    # rec = tomopy.recon(prj, theta, center=best_center/pow(2,level), algorithm='gridrec', filter_name='parzen')
    # rec = tomopy.recon(prj*1e2, theta, center=best_center/pow(2,level), algorithm='ospml_hybrid', 
    #                    num_iter=20, reg_par=[1e3, 1e-3])
    
    # Write data as stack of TIFs.
    dxchange.write_tiff_stack(rec, fname=output_name)

    print "Slice saved as [%s_00000.tiff]" % output_name
    
    
def rec_full(file_name, sino_start, sino_end, astra_method, extra_options, num_iter=1):

    start_time = time.time()
    print '\n#### Processing '+ file_name

    chunks = 8 # number of data chunks for the reconstruction

    nSino_per_chunk = (sino_end - sino_start)/chunks
    print "Reconstructing [%d] slices from slice [%d] to [%d] in [%d] chunks of [%d] slices each" % ((sino_end - sino_start), sino_start, sino_end, chunks, nSino_per_chunk)
    strt = 0
    for iChunk in range(0,chunks):
        print '\n  -- chunk # %i' % (iChunk+1)
        sino_chunk_start = sino_start + nSino_per_chunk*iChunk 
        sino_chunk_end = sino_start + nSino_per_chunk*(iChunk+1)
        print '\n  --------> [%i, %i]' % (sino_chunk_start, sino_chunk_end)
        
        if sino_chunk_end > sino_end: 
            break
                
        # Read HDF5 file.
        prj, flat, dark, theta = dxchange.read_aps_32id(file_name, sino=(sino_chunk_start, sino_chunk_end))
        #prj = prj[:-1,:,:]
        #flat = flat[20:-1,:,:]
        dark = dark[1:,:,:]

        # Manage the missing angles:
        if theta == None:
            theta  = tomopy.angles(prj.shape[0])
        else:
            pass
#        theta = np.concatenate((theta[0:miss_angles[0]], theta[miss_angles[1]+1:-1]))
#        theta = np.concatenate((theta[0:miss_angles[0]], theta[miss_angles[1]+1:-1]))
        prj = prj[miss_angles[0]:miss_angles[1],:,:]
        theta = theta[miss_angles[0]:miss_angles[1]]
        
        # normalize the prj
        #prj = -np.log(tomopy.normalize(prj, flat, dark))
        #prj[prj<=0]=0.001
        #prj = tomopy.minus_log(prj)

        # remove ring artefacts
        prj = tomopy.remove_stripe_ti(prj,2)
        prj = tomopy.remove_stripe_sf(prj,10)

        # Median filter:
        if medfilt_size:
            prj = tomopy.median_filter(prj,size=medfilt_size)
        
        
        if level>0:
            prj = tomopy.downsample(prj, level=level)
            prj = tomopy.downsample(prj, level=level, axis=1)
        
        # reconstruct 
        rec = tomopy.recon(prj, theta, center=best_center/pow(2,level), algorithm=tomopy.astra, options={'proj_type':proj_type,'method':astra_method,'extra_options':extra_options,'num_iter':num_iter}, emission=False)
        #rec = tomopy.recon(prj, theta, center=best_center/pow(2,level), algorithm='gridrec', filter_name='parzen')

        print output_name

        # Write data as stack of TIFs.
        dxchange.write_tiff_stack(rec, fname=output_name, start=strt)
        strt += prj.shape[1]

    print("%i minutes" % ((time.time() - start_time)/60))



astra_method='SIRT-FBP'
if astra_method=='SIRT-FBP':
    astra.plugin.register(sirtfbp.plugin)
    extra_options = {'filter_dir':'./filters'}
    num_iter = 100

if reconstruction_test: rec_test(file_name, sino_start, sino_end, astra_method, extra_options, num_iter)
else: rec_full(file_name, sino_start, sino_end, astra_method, extra_options, num_iter)