def main():
# only used to locate the wavelenght.dpt and angle.dpt files
raw_tiff_base_name = "/local/dataraid/databank/dataExchange/microCT/SRC/raw/FPA_16_18_18_TOMO_243_Fiber_2500_50_50_"
hdf5_base_name = "/local/dataraid/databank/dataExchange/microCT/SRC/dx/FPA_16_18_18_TOMO_243_Fiber_2500_50_50_"
log_file = raw_tiff_base_name + "wavelength.dpt"
angle_file = raw_tiff_base_name + "angle.dpt"
dir_name = os.path.dirname(hdf5_base_name)
sample_name_prefix = os.path.basename(hdf5_base_name)
print dir_name
print sample_name_prefix
file = open(log_file, 'r')
for line in file:
linelist=line.split(",")
hdf5_file_name = hdf5_base_name+linelist[0]+"cm-1.h5"
sample_name = hdf5_base_name+linelist[0]+"cm-1"
# set to read slices between slices_start and slices_end
# if omitted all data set will be converted
slices_start = 30
slices_end = 36
# to create a data exchange file use convert_SRC.py module,
data, white, dark, theta = tomopy.xtomo_reader(hdf5_file_name,
slices_start=slices_start,
slices_end=slices_end)
# TomoPy xtomo object creation and pipeline of methods.
d = tomopy.xtomo_dataset(log='debug')
d.dataset(data, white, dark, theta)
d.normalize()
d.correct_drift()
#d.optimize_center()
#d.phase_retrieval()
#d.correct_drift()
d.center=64
d.gridrec()
# Write to stack of TIFFs.
rec_name = dir_name + "/rec/" + sample_name_prefix + linelist[0] + "cm-1"
# Write to stack of TIFFs.
mydata = ex.Export()
mydata.xtomo_tiff(data = d.data_recon, output_file = rec_name, axis=0)
file.close()
def hdf5_to_recon(inputDir = '.', inputFileName='out.h5', startSlice=None, endSlice=None, outputDir='./recon', outBase = 'recon_', center=None):
# Make the output directory if it doesn't already exist.
if not os.path.exists(outputDir):
os.makedirs(outputDir)
# This is the path to the input HDF5 file.
inputFilePath = os.path.join(inputDir, inputFileName)
# This is the path to the output files
outputPath = os.path.join(outputDir, outBase)
# Read HDF5 file.
data, white, dark, theta = tomopy.xtomo_reader(inputFilePath, slices_start=startSlice, slices_end=endSlice)
# Xtomo object creation and pipeline of methods.
d = tomopy.xtomo_dataset(log='debug')
d.dataset(data, white, dark, theta)
d.normalize()
d.correct_drift()
d.phase_retrieval()
d.correct_drift()
# Appliy a median filter to the (images?) to improve the chance of success of the center finder.
d.median_filter()
# Find the center of rotation
if center == None:
d.optimize_center()
else:
d.center=center
# Do the tomographic reconstruction
d.gridrec()
# Write to stack of TIFFs.
tomopy.xtomo_writer(d.data_recon, outputPath, axis=0, x_start=startSlice)
def main():
# read a series of tiff
# oster: pj: from 0 -> 1440; bf from 0 -> 19; df from 0 -> 19
file_name = '/local/dataraid/databank/PetraIII/2011_KW16_oster/oster02_0001/scan_0002/ccd/pco01/ccd_.tif'
dark_file_name = '/local/dataraid/databank/PetraIII/2011_KW16_oster/oster02_0001/scan_0000/ccd/pco01/ccd_.tif'
white_file_name = '/local/dataraid/databank/PetraIII/2011_KW16_oster/oster02_0001/scan_0001/ccd/pco01/ccd_.tif'
hdf5_file_name = '/local/dataraid/databank/dataExchange/microCT/PetraIII_oster02_0001.h5'
sample_name = 'PetraIII P06 oster02_0001'
projections_start = 0
projections_end = 1441
white_start = 0
white_end = 20
white_step = 1
dark_start = 0
dark_end = 20
dark_step = 1
# to reconstruct slices from slices_start to slices_end
# if omitted all data set is recontructed
slices_start = 1001
slices_end = 1501
# mydata = dx.Import()
# # Read series of images
# data, white, dark, theta = mydata.series_of_images(file_name,
# projections_start = projections_start,
# projections_end = projections_end,
# slices_start = slices_start,
# slices_end = slices_end,
# #projections_angle_range=360,
# white_file_name = white_file_name,
# white_start = white_start,
# white_end = white_end,
# white_step = white_step,
# dark_file_name = dark_file_name,
# dark_start = dark_start,
# dark_end = dark_end,
# dark_step = dark_step,
# sample_name = sample_name,
# projections_digits = 4,
# projections_zeros = True,
# log='INFO'
# )
# if you have already created a data exchange file using convert_PetraIII.py module,
# comment the call above and read the data set as data exchange using:
# Read HDF5 file.
data, white, dark, theta = tomopy.xtomo_reader(hdf5_file_name,
slices_start=slices_start,
slices_end=slices_end
)
# TomoPy xtomo object creation and pipeline of methods.
d = tomopy.xtomo_dataset(log='debug')
d.dataset(data, white, dark, theta)
d.normalize()
d.correct_drift()
#d.optimize_center()
d.phase_retrieval(pixel_size=0.9e-4, dist=6.9, energy=15.25)
#d.correct_drift()
d.center=1872.87890625
d.gridrec()
# Write to stack of TIFFs.
# tomopy.xtomo_writer(d.data_recon, 'tmp/oster02_0001_', axis=0)
tomopy.xtomo_writer(d.data_recon, 'tmp/oster02_0001_int_', axis=0, x_start = 1001, overwrite=True, dtype='uint8', data_min=-0.0001, data_max=0.0003)
######################### you input section - End #########################
######################### initialization - Start #########################
file_name = os.path.join(data_dir, file)
output_file = data_dir+'/recon_'+file.split(".")[-2]+'/recon_'+file.split(".")[-2] +'_'
log_filename = data_dir+'/recon_'+file.split(".")[-2]+'/'+file.split(".")[-2] +'.log'
# object d is for containing data that will be used for phase retrieval
d = tomopy.xtomo_dataset(log='debug')
# object d_recon is for containing data for reconstruction
d_recon = tomopy.xtomo_dataset(log='debug')
######################### initialization - End #########################
########################## finding num_overlap_pixels - Start #########################
#### phase retrieval - Start ####
data, white, dark, theta = tomopy.xtomo_reader(file_name,slices_start=300,slices_end=310,white_start=3,white_end=9,dark_start=3,dark_end=9)
data[0,:,:] = data[1,:,:] # in case the first projection image is corrupted
d.dataset(data, white, dark, theta)
d.normalize()
d.stripe_removal(wname="db5",level=10,sigma=4.)
data_size = d.data.shape
d.phase_retrieval(dist=z, energy=eng, pixel_size=pxl, alpha=rat,padding=True)
data_tem = d.data
#### phase retrieval - End ####
#### trial recons - Start ####
for ii in range(nop_start,nop_end):
d.dataset(data_tem,np.ones([d.data.shape[1],d.data.shape[2]]),None,None)
d.correct_fov(num_overlap_pixels=ii)
# tomopy.xtomo_writer(d.data[:,5:7,:], output_file+'sinogram_num_overlap_pixel_'+str(ii)+'_', axis=1,x_start=0,overwrite = True)
def main():
file_name = '/local/dataraid/databank/als/data/raw/sacarroll/20140731_001306_2477A_x00y08/20140731_001306_2477A_x00y08_0000_.tif'
dark_file_name = '/local/dataraid/databank/als/data/raw/sacarroll/20140731_001306_2477A_x00y08/20140731_001306_2477A_x00y08drk_.tif'
white_file_name = '/local/dataraid/databank/als/data/raw/sacarroll/20140731_001306_2477A_x00y08/20140731_001306_2477A_x00y08bak_.tif'
log_file = '/local/dataraid/databank/als/data/raw/sacarroll/20140731_001306_2477A_x00y08/20140731_001306_2477A_x00y08.sct'
hdf5_file_name = '/local/dataraid/databank/dataExchange/microCT/ALS_20140731.h5'
verbose = True
# start = time.clock()
# Read ALS log file data
file = open(log_file, 'r')
for line in file:
if '-scanner' in line:
Source = re.sub(r'-scanner ', "", line)
if verbose: print 'Facility', Source
if '-object' in line:
Sample = re.sub(r'-object ', "", line)
if verbose: print 'Sample', Sample
if '-senergy' in line:
Energy = re.findall(r'\d+.\d+', line)
if verbose: print 'Energy', Energy[0]
if '-scurrent' in line:
Current = re.findall(r'\d+.\d+', line)
if verbose: print 'Current', Current[0]
if '-nangles' in line:
Angles = re.findall(r'\d+', line)
if verbose: print 'Angles', Angles[0]
if '-i0cycle' in line:
WhiteStep = re.findall(r'\s+\d+', line)
if verbose: print 'White Step', WhiteStep[0]
if '-num_bright_field' in line:
WhiteEnd = re.findall(r'\d+', line)
if '-num_dark_fields' in line:
DarkEnd = re.findall(r'\d+', line)
file.close()
dark_start = 0
dark_end = int(DarkEnd[0])
dark_step = 1
white_start = 0
white_end = int(WhiteEnd[0])
white_step = int(WhiteStep[0])
projections_start = 0
projections_end = int(Angles[0])
# set to convert slices between slices_start and slices_end
# if omitted all data set will be converted
slices_start = 245
slices_end = 265
# mydata = dx.Import()
# Read series of images
# data, white, dark, theta = mydata.series_of_images(file_name = file_name,
# projections_start = projections_start,
# projections_end = projections_end,
## slices_start = slices_start,
## slices_end = slices_end,
# white_file_name = white_file_name,
# white_start = white_start,
# white_end = white_end,
# dark_file_name = dark_file_name,
# dark_start = dark_start,
# dark_end = dark_end,
# projections_digits = 4,
# log='INFO'
# )
start = time.clock()
# if you have already created a data exchange file using convert_SLS.py module,
# comment the call above and read the data set as data exchange
# Read HDF5 file.
data, white, dark, theta = tomopy.xtomo_reader(hdf5_file_name,
slices_start=slices_start,
slices_end=slices_end)
elapsed = (time.clock() - start)
print "Total Time to read raw files:", elapsed
# TomoPy xtomo object creation and pipeline of methods.
d = tomopy.xtomo_dataset(log='debug')
d.dataset(data, white, dark, theta)
d.normalize()
d.correct_drift()
#d.optimize_center()
#d.phase_retrieval()
#d.correct_drift()
d.center=1282.5
d.gridrec()
# Write to stack of TIFFs.
tomopy.xtomo_writer(d.data_recon, 'tmp/ALS_new', axis=0)
# -*- coding: utf-8 -*-
import tomopy
file_name = '/local/data/Tapuia_dentary_ant3_M_200mm_4.hdf5'
slices_start = 300
chunk_size = 32 # keep it reasoably large depending on the available memory
for m in range(4): # change range manually 4*32 slices will be reconstructed starting from 300
data, white, dark, theta = tomopy.xtomo_reader(file_name, projections_start=1, slices_start=slices_start, slices_end=slices_start+chunk_size)
d = tomopy.xtomo_dataset(log='debug')
d.dataset(data, white, dark, theta=theta)
d.zinger_removal(zinger_level=500, median_width=5)
d.normalize(negvals=1, cutoff=1)
d.stripe_removal()
d.center=1013.90625
d.gridrec()
d.apply_mask(ratio=0.95)
# use the first chunks min max value for all chunks
if m == 0:
data_min = d.data_recon.min()
data_max = d.data_recon.max()
tomopy.xtomo_writer(d.data_recon, 'tmp/test/rec_', dtype='uint16', axis=0, x_start=slices_start, data_min=data_min, data_max=data_max)
slices_start += chunk_size
# -*- coding: utf-8 -*-
import tomopy
# Read HDF5 file.
data, white, dark, theta = tomopy.xtomo_reader('demo/data.h5',
slices_start=0,
slices_end=16)
# Xtomo object creation and pipeline of methods.
d = tomopy.xtomo_dataset(log='debug')
d.dataset(data, white, dark, theta)
d.normalize()
d.correct_drift()
d.phase_retrieval()
d.correct_drift()
d.center = 661.5
d.gridrec()
# Write to stack of TIFFs.
tomopy.xtomo_writer(d.data_recon, 'tmp/test_', axis=0)
RingW = 10 # for ring artifact removal M. Rivers algo
chunk = 6 # number of data chunks for the reconstruction
ExchangeRank = 0 # exchange rank corresponding to the dataset
########################################################################################################
print '\n#### Processing '+file_name
#### for 1 slice reconstruction:
#-------------------------------
if 1:
# slice_first = 1000
# slice_last = 1400
# Read HDF5 file.
data, white, dark, theta = tomopy.xtomo_reader(file_name,
exchange_rank = ExchangeRank,
slices_start=slice_first,
slices_end=slice_last)
# Manage the missing angles:
data_size = np.shape(data)
theta = np.linspace(0,180,data_size[0])
data = np.concatenate((data[0:miss_angles[0],:,:], data[miss_angles[1]+1:-1,:,:]), axis=0)
theta = np.concatenate((theta[0:miss_angles[0]], theta[miss_angles[1]+1:-1]))
# Xtomo object creation and pipeline of methods.
# d = tomopy.xtomo_dataset(log='debug')
d = tomopy.xtomo_dataset(log='debug')
d.dataset(data, white, dark, theta)
if perform_norm: d.normalize() # flat & dark field correction
if drift_correct: d.correct_drift()
def main():
# read a series of tiff
# oster: pj: from 0 -> 1440; bf from 0 -> 19; df from 0 -> 19
file_name = '/local/dataraid/databank/PetraIII/2011_KW16_oster/oster02_0001/scan_0002/ccd/pco01/ccd_.tif'
dark_file_name = '/local/dataraid/databank/PetraIII/2011_KW16_oster/oster02_0001/scan_0000/ccd/pco01/ccd_.tif'
white_file_name = '/local/dataraid/databank/PetraIII/2011_KW16_oster/oster02_0001/scan_0001/ccd/pco01/ccd_.tif'
hdf5_file_name = '/local/dataraid/databank/dataExchange/microCT/PetraIII_oster02_0001.h5'
sample_name = 'PetraIII P06 oster02_0001'
projections_start = 0
projections_end = 1441
white_start = 0
white_end = 20
white_step = 1
dark_start = 0
dark_end = 20
dark_step = 1
# to reconstruct slices from slices_start to slices_end
# if omitted all data set is recontructed
slices_start = 1001
slices_end = 1501
# mydata = dx.Import()
# # Read series of images
# data, white, dark, theta = mydata.series_of_images(file_name,
# projections_start = projections_start,
# projections_end = projections_end,
# slices_start = slices_start,
# slices_end = slices_end,
# #projections_angle_range=360,
# white_file_name = white_file_name,
# white_start = white_start,
# white_end = white_end,
# white_step = white_step,
# dark_file_name = dark_file_name,
# dark_start = dark_start,
# dark_end = dark_end,
# dark_step = dark_step,
# sample_name = sample_name,
# projections_digits = 4,
# projections_zeros = True,
# log='INFO'
# )
# if you have already created a data exchange file using convert_PetraIII.py module,
# comment the call above and read the data set as data exchange using:
# Read HDF5 file.
data, white, dark, theta = tomopy.xtomo_reader(hdf5_file_name,
slices_start=slices_start,
slices_end=slices_end)
# TomoPy xtomo object creation and pipeline of methods.
d = tomopy.xtomo_dataset(log='debug')
d.dataset(data, white, dark, theta)
d.normalize()
d.correct_drift()
#d.optimize_center()
d.phase_retrieval(pixel_size=0.9e-4, dist=6.9, energy=15.25)
#d.correct_drift()
d.center = 1872.87890625
d.gridrec()
# Write to stack of TIFFs.
# tomopy.xtomo_writer(d.data_recon, 'tmp/oster02_0001_', axis=0)
tomopy.xtomo_writer(d.data_recon,
'tmp/oster02_0001_int_',
axis=0,
x_start=1001,
overwrite=True,
dtype='uint8',
data_min=-0.0001,
data_max=0.0003)
# -*- coding: utf-8 -*-
import tomopy
#main is needed on windows
if __name__ == '__main__':
# Read HDF5 file.
data, white, dark, theta = tomopy.xtomo_reader('demo/data.h5',
slices_start=0,
slices_end=16)
# Xtomo object creation and pipeline of methods.
d = tomopy.xtomo_dataset(log='debug')
d.dataset(data, white, dark, theta)
d.normalize()
d.correct_drift()
d.phase_retrieval()
d.correct_drift()
d.center = 661.5
d.gridrec()
# Write to stack of TIFFs.
tomopy.xtomo_writer(d.data_recon, 'tmp/test_', axis=0)
chunk_size = 60
margin_slices = 20
num_chunk = np.int(num_proj/chunk_size) + 1
if num_proj == chunk_size:
num_chunk = 1
z = 15
eng = 27.4
#pxl = 5.5e-4 #for 2X lens
pxl = 2.2e-4 #for 5X lens
rat = 5e-04
center = 1034
##________________________rescale_for_16-bit_image____________________
data, white, dark, theta = tomopy.xtomo_reader(file_name, projections_start=1, slices_start=(num_proj/3), slices_end=(num_proj/3)+chunk_size)
d = tomopy.xtomo_dataset(log='debug')
d.dataset(data, white, dark, theta=theta)
d.zinger_removal(zinger_level=500, median_width=5)
d.normalize(negvals=1, cutoff=1)
d.stripe_removal()
d.center=center
d.gridrec()
d.apply_mask(ratio=0.95)
data_min = d.data_recon.min()
data_max = d.data_recon.max()
##________________________reconstruction______________________________
for ii in xrange(num_chunk):
# d = tomopy.xtomo_dataset(log='debug') # d.dataset(data, white, dark, theta=theta) # d.zinger_removal(zinger_level=500, median_width=5) # d.normalize(negvals=1, cutoff=1) # # d.stripe_removal() # d.phase_retrieval(alpha=0.005) # d.center=1010 # d.gridrec(ringWidth=30) # d.apply_mask(ratio=0.95) # tomopy.xtomo_writer(d.data_recon, 'tmp/Tapuia_dentary_ant2_200mm_4_/rec_', dtype='uint16', axis=0) file_name = '/local/data/Tapuiasaurus_dentary_ant_200mm_1.hdf5' data, white, dark, theta = tomopy.xtomo_reader(file_name, projections_start=1, slices_start=300, slices_end=332) d = tomopy.xtomo_dataset(log='debug') d.dataset(data, white, dark, theta=theta) d.zinger_removal(zinger_level=500, median_width=5) d.normalize(negvals=1, cutoff=1) d.stripe_removal() d.phase_retrieval(alpha=0.005) d.center=1010 d.gridrec(ringWidth=0) d.apply_mask(ratio=0.95) tomopy.xtomo_writer(d.data_recon, 'tmp/Tapuiasaurus_dentary_ant_200mm_1/rec_', dtype='uint16', axis=0)
level = 1 # 2^level binning
RingW = 10 # for ring artifact removal M. Rivers algo
chunk = 6 # number of data chunks for the reconstruction
ExchangeRank = 0 # exchange rank corresponding to the dataset
########################################################################################################
print '\n#### Processing ' + file_name
#### for 1 slice reconstruction:
#-------------------------------
if 1:
# slice_first = 1000
# slice_last = 1400
# Read HDF5 file.
data, white, dark, theta = tomopy.xtomo_reader(file_name,
exchange_rank=ExchangeRank,
slices_start=slice_first,
slices_end=slice_last)
# Manage the missing angles:
data_size = np.shape(data)
theta = np.linspace(0, 180, data_size[0])
data = np.concatenate(
(data[0:miss_angles[0], :, :], data[miss_angles[1] + 1:-1, :, :]),
axis=0)
theta = np.concatenate(
(theta[0:miss_angles[0]], theta[miss_angles[1] + 1:-1]))
# Xtomo object creation and pipeline of methods.
# d = tomopy.xtomo_dataset(log='debug')
d = tomopy.xtomo_dataset(log='debug')
d.dataset(data, white, dark, theta)
center = 1028
for ii in xrange(num_chunk):
if ii == 0:
SliceStart = offset + ii*chunk_size
SliceEnd = offset + (ii+1)*chunk_size
else:
SliceStart = offset + ii*(chunk_size-margin_slices)
SliceEnd = offset + SliceStart + chunk_size
if SliceEnd > (offset+num_proj):
SliceEnd = offset+num_proj
data, white, dark, theta = tomopy.xtomo_reader(file_name,slices_start=SliceStart,slices_end=SliceEnd,white_start=3,white_end=9,dark_start=3,dark_end=9)
data[0,:,:] = data[1,:,:]
d.dataset(data, white, dark, theta)
d.zinger_removal(median_width=10)
d.normalize()
d.correct_drift(10)
d.stripe_removal(wname="sym16",level=10,sigma=4)
d.phase_retrieval(dist=z, energy=eng, pixel_size=pxl, alpha=rat,padding=True)
d.center = center
d.gridrec()
tomopy.xtomo_writer(d.data_recon[np.int(margin_slices/2):(SliceEnd-SliceStart-np.int(margin_slices/2)),:,:], output_file, axis=0,dtype='float32', x_start=SliceStart+np.int(margin_slices/2),overwrite = True)
d.FLAG_THETA = False
def recon_180(data_dir='.', file = 'out.h5', output_dir='/Volumes/Socha_MP8/', num_proj=1800, startSlice=None, endSlice=None, z=15, eng=27.4, lens=2, center=None):
##_________________________inputs_and_parameters_______________________
file_name = os.path.join(data_dir, file)
output_file = output_dir+'/recon_'+file.split(".")[-2]+'/recon_'+file.split(".")[-2] +'_'
d = tomopy.xtomo_dataset(log='debug')
# Program does not complete a sample if actual number of slices is input. This somewhat arbitrary scaled value seems to work well.
#num_proj = (3/2)*num_proj
# These values work well with the computer's available memory and produce quality images.
chunk_size = 60
margin_slices = 20
# Define pixel size from function argument lens.
pxl = 0
if lens == 2:
pxl = 5.5e-4 #for 2X lens
elif lens == 5:
pxl = 2.2e-4 #for 5X lens
rat = 5e-04
num_chunk = np.int(num_proj/chunk_size) + 1
if num_proj == chunk_size:
num_chunk = 1
#Set defalt start and end slice values.
if endSlice == None:
endSlice = num_proj
if startSlice == None:
startSlice=0
offset = startSlice
else:
offset = startSlice - (margin_slices/2)
##________________________rescale_for_16-bit_image____________________
data, white, dark, theta = tomopy.xtomo_reader(file_name, projections_start=1, slices_start=(num_proj/2), slices_end=(num_proj/2)+chunk_size)
d = tomopy.xtomo_dataset(log='debug')
d.dataset(data, white, dark, theta=theta)
#d.zinger_removal(zinger_level=500, median_width=5)
d.normalize(negvals=1, cutoff=1)
# Helps minimize ring artifacts.
d.stripe_removal()
# Finds center of rotation
if center == None:
d.optimize_center(ratio=1)
else:
d.center=center
d.gridrec()
data_min = d.data_recon.min()
data_max = d.data_recon.max()
##________________________reconstruction______________________________
for ii in xrange(num_chunk):
if ii == 0:
SliceStart = offset + ii*chunk_size
SliceEnd = offset + (ii+1)*chunk_size
else:
SliceStart = offset + ii*(chunk_size-margin_slices)
SliceEnd = offset + SliceStart + chunk_size
if SliceEnd >= (endSlice-(margin_slices/2)) and SliceEnd < (endSlice-margin_slices+chunk_size):
SliceEnd = (endSlice-(margin_slices/2))
if SliceEnd >= (endSlice-margin_slices+chunk_size):
break
data, white, dark, theta = tomopy.xtomo_reader(file_name,slices_start=SliceStart,slices_end=SliceEnd,white_start=3,white_end=9,dark_start=3,dark_end=9)
data[0,:,:] = data[1,:,:]
d.dataset(data, white, dark, theta)
#d.zinger_removal(median_width=10)
d.normalize()
d.correct_drift(10)
d.stripe_removal(wname="sym16",level=11,sigma=2)
d.phase_retrieval(dist=z, energy=eng, pixel_size=pxl, alpha=rat,padding=True)
# Finds center of rotation
if center == None:
d.optimize_center(ratio=0.8)
else:
d.center=center
d.gridrec()
d.apply_mask(ratio=0.95)
#data[0,:,:] = data[1,:,:]
#d = tomopy.xtomo_dataset(log='debug')
#d.dataset(data, white, dark, theta)
##d.zinger_removal(median_width=15,zinger_level=200)
#d.normalize()
##d.stripe_removal(wname="sym16",level=10,sigma=2)
##d.stripe_removal(level=10,sigma=1)
#d.phase_retrieval(dist=z, energy=eng, pixel_size=pxl, alpha=rat,padding=True)
##d.stripe_removal(wname="sym16",level=10,sigma=4)
#data_size = d.data.shape
##d.median_filter(10)
##d.optimize_center(ratio=0.3)
##print d.center
tomopy.xtomo_writer(d.data_recon[np.int(margin_slices/2):(SliceEnd-SliceStart-np.int(margin_slices/2)),:,:], output_file, dtype='unit16', axis=0, x_start=SliceStart+np.int(margin_slices/2), data_min=data_min, data_max=data_max, overwrite=True)
d.FLAG_THETA = False
print 'Finished reconstructing slices from ' + file
