newpath = os.path.join(tempdir, os.path.basename(filepath)[:-3])

# The following with pigz is ~4x faster than the python code above (with gzip, it's about 2.5x faster)

if os.path.isfile('/usr/bin/pigz'):

subprocess.call('pigz -d -c %s > %s' % (filepath, newpath), shell=True)

elif os.path.isfile('/usr/bin/gzip') or os.path.isfile('/bin/gzip'):

subprocess.call('gzip -d -c %s > %s' % (filepath, newpath), shell=True)

else:

with open(newpath, 'wb') as fd:

with gzip.open(filepath, 'rb') as gzfile:

fd.writelines(gzfile)

"""

Read pfile data and/or header.

This class reads the data and/or header from a pfile, runs k-space reconstruction,

and generates a NIfTI object, including header information.

Example:

import pfile

pf = pfile.PFile(filename='P56832.7')

pf.to_nii(outbase='P56832.7')

"""

filetype = u'pfile'

parse_priority = 5

# TODO: Simplify init, just to parse the header

def __init__(self, filepath, num_virtual_coils=16):

try:

self.compressed = is_compressed(filepath)

self._hdr = pfile.parse(filepath, self.compressed)

except (IOError, pfile.PFileError) as e:

raise NIMSPFileError(str(e))

self.exam_no = self._hdr.exam.ex_no

self.patient_id = self._hdr.exam.patidff.strip('\x00')

super(NIMSPFile, self).__init__()

self.filepath = os.path.abspath(filepath)

self.dirpath = os.path.dirname(self.filepath)

self.filename = os.path.basename(self.filepath)

self.basename = self.filename[:-3] if self.compressed else self.filename

self.imagedata = None

self.fm_data = None

self.num_vcoils = num_virtual_coils

self.psd_name = os.path.basename(self._hdr.image.psdname.partition('\x00')[0])

self.psd_type = nimsmrdata.infer_psd_type(self.psd_name)

self.pfilename = 'P%05d' % self._hdr.rec.run_int

self.series_no = self._hdr.series.se_no

self.acq_no = self._hdr.image.scanactno

self.exam_uid = unpack_uid(self._hdr.exam.study_uid)

self.series_uid = unpack_uid(self._hdr.series.series_uid)

self.series_desc = self._hdr.series.se_desc.strip('\x00')

self.subj_firstname, self.subj_lastname = self.parse_subject_name(self._hdr.exam.patnameff.strip('\x00'))

self.subj_dob = self.parse_subject_dob(self._hdr.exam.dateofbirth.strip('\x00'))

self.subj_sex = ('male', 'female')[self._hdr.exam.patsex-1] if self._hdr.exam.patsex in [1,2] else None

if self._hdr.image.im_datetime > 0:

self.timestamp = datetime.datetime.utcfromtimestamp(self._hdr.image.im_datetime)

else: # HOShims don't have self._hdr.image.im_datetime

month, day, year = map(int, self._hdr.rec.scan_date.strip('\x00').split('/'))

hour, minute = map(int, self._hdr.rec.scan_time.strip('\x00').split(':'))

self.timestamp = datetime.datetime(year + 1900, month, day, hour, minute) # GE's epoch begins in 1900

self.ti = self._hdr.image.ti / 1e6

self.te = self._hdr.image.te / 1e6

self.tr = self._hdr.image.tr / 1e6 # tr in seconds

self.flip_angle = float(self._hdr.image.mr_flip)

self.pixel_bandwidth = self._hdr.rec.bw

# Note: the freq/phase dir isn't meaningful for spiral trajectories.

# GE numbers the dims 1,2, so freq_dir==1 is the first dim. We'll use

# the convention where first dim = 0, second dim = 1, etc. for phase_encode.

self.phase_encode = 1 if self._hdr.image.freq_dir==1 else 0

self.mt_offset_hz = self._hdr.image.offsetfreq

self.num_slices = self._hdr.image.slquant

self.num_averages = self._hdr.image.averages

self.num_echos = self._hdr.rec.nechoes

self.receive_coil_name = self._hdr.image.cname.strip('\x00')

self.num_receivers = self._hdr.rec.dab[0].stop_rcv - self._hdr.rec.dab[0].start_rcv + 1

self.operator = self._hdr.exam.operator_new.strip('\x00')

self.protocol_name = self._hdr.series.prtcl.strip('\x00')

self.scanner_name = self._hdr.exam.hospname.strip('\x00') + ' ' + self._hdr.exam.ex_sysid.strip('\x00')

self.scanner_type = 'GE MEDICAL' # FIXME

self.acquisition_type = ''

self.size = [self._hdr.image.dim_X, self._hdr.image.dim_Y] # imatrix_Y

self.fov = [self._hdr.image.dfov, self._hdr.image.dfov_rect]

self.scan_type = self._hdr.image.psd_iname.strip('\x00')

self.num_bands = 1

self.num_mux_cal_cycle = 0

self.num_timepoints = self._hdr.rec.npasses

# Some sequences (e.g., muxepi) acuire more timepoints that will be available in the resulting data file.

# The following will indicate how many to expect in the final image.

self.num_timepoints_available = self.num_timepoints

self.deltaTE = 0.0

self.scale_data = False

# Compute the voxel size rather than use image.pixsize_X/Y

self.mm_per_vox = [self.fov[0] / self.size[0], self.fov[1] / self.size[1], self._hdr.image.slthick + self._hdr.image.scanspacing]

image_tlhc = np.array([self._hdr.image.tlhc_R, self._hdr.image.tlhc_A, self._hdr.image.tlhc_S])

image_trhc = np.array([self._hdr.image.trhc_R, self._hdr.image.trhc_A, self._hdr.image.trhc_S])

image_brhc = np.array([self._hdr.image.brhc_R, self._hdr.image.brhc_A, self._hdr.image.brhc_S])

# psd-specific params get set here

if self.psd_type == 'spiral':

self.num_timepoints = int(self._hdr.rec.user0) # not in self._hdr.rec.nframes for sprt

self.num_timepoints_available = self.num_timepoints

self.deltaTE = self._hdr.rec.user15

self.band_spacing = 0

self.scale_data = True

# spiral is always a square encode based on the frequency encode direction (size_x)

# Atsushi also likes to round up to the next higher power of 2.

# self.size_x = int(pow(2,ceil(log2(pf.size_x))))

# The rec.im_size field seems to have the correct reconned image size, but

# this isn't guaranteed to be correct, as Atsushi's recon does whatever it

# damn well pleases. Maybe we could add a check to infer the image size,

# assuming it's square?

self.size[0] = self.size[1] = self._hdr.rec.im_size

self.mm_per_vox[0:2] = [self.fov[0] / self.size[0]] * 2

elif self.psd_type == 'basic':

# first 6 are ref scans, so ignore those. Also, two acquired timepoints are used

# to generate each reconned time point.

self.num_timepoints = (self._hdr.rec.npasses * self._hdr.rec.nechoes - 6) / 2

self.num_timepoints_available = self.num_timepoints

self.num_echos = 1

elif self.psd_type == 'muxepi':

self.num_bands = int(self._hdr.rec.user6)

self.num_mux_cal_cycle = int(self._hdr.rec.user7)

self.band_spacing_mm = self._hdr.rec.user8

self.num_timepoints = self._hdr.rec.npasses + self.num_bands * self._hdr.rec.ileaves * (self.num_mux_cal_cycle-1)

self.num_timepoints_available = self._hdr.rec.npasses - self.num_bands * self._hdr.rec.ileaves * (self.num_mux_cal_cycle-1) + self.num_mux_cal_cycle

# TODO: adjust the image.tlhc... fields to match the correct geometry.

elif self.psd_type == 'mrs':

self._hdr.image.scanspacing = 0.

self.mm_per_vox = [self._hdr.rec.roileny, self._hdr.rec.roilenx, self._hdr.rec.roilenz]

image_tlhc = np.array((-self._hdr.rec.roilocx - self.mm_per_vox[0]/2.,

self._hdr.rec.roilocy + self.mm_per_vox[1]/2.,

self._hdr.rec.roilocz - self.mm_per_vox[1]/2.))

image_trhc = image_tlhc - [self.mm_per_vox[0], 0., 0.]

image_brhc = image_trhc + [0., self.mm_per_vox[1], 0.]

# Tread carefully! Most of the stuff down here depends on various fields being corrected in the

# sequence-specific set of hacks just above. So, move things with care!

# Note: the following is true for single-shot planar acquisitions (EPI and 1-shot spiral).

# For multishot sequences, we need to multiply by the # of shots. And for non-planar aquisitions,

# we'd need to multiply by the # of phase encodes (accounting for any acceleration factors).

# Even for planar sequences, this will be wrong (under-estimate) in case of cardiac-gating.

self.prescribed_duration = self.num_timepoints * self.tr

self.total_num_slices = self.num_slices * self.num_timepoints

# The actual duration can only be computed after the data are loaded. Settled for rx duration for now.

self.duration = self.prescribed_duration

self.effective_echo_spacing = self._hdr.image.effechospace / 1e6

self.phase_encode_undersample = 1. / self._hdr.rec.ileaves

# TODO: Set this correctly! (it's in the dicom at (0x0043, 0x1083))

self.slice_encode_undersample = 1.

self.acquisition_matrix = [self._hdr.rec.rc_xres, self._hdr.rec.rc_yres]

# Diffusion params

self.dwi_numdirs = self._hdr.rec.numdifdirs

# You might think that the b-valuei for diffusion scans would be stored in self._hdr.image.b_value.

# But alas, this is GE. Apparently, that var stores the b-value of the just the first image, which is

# usually a non-dwi. So, we had to modify the PSD and stick the b-value into an rhuser CV. Sigh.

self.dwi_bvalue = self._hdr.rec.user22

self.is_dwi = True if self.dwi_numdirs >= 6 else False

# if bit 4 of rhtype(int16) is set, then fractional NEX (i.e., partial ky acquisition) was used.

self.partial_ky = self._hdr.rec.scan_type & np.uint16(16) > 0

self.caipi = self._hdr.rec.user13 # true: CAIPIRINHA-type acquisition; false: Direct aliasing of simultaneous slices.

self.cap_blip_start = self._hdr.rec.user14 # Starting index of the kz blips. 0~(mux-1) correspond to -kmax~kmax.

self.cap_blip_inc = self._hdr.rec.user15 # Increment of the kz blip index for adjacent acquired ky lines.

self.mica = self._hdr.rec.user17 # MICA bit-reverse?

self.slice_duration = self.tr / self.num_slices

lr_diff = image_trhc - image_tlhc

si_diff = image_trhc - image_brhc

if not np.all(lr_diff==0) and not np.all(si_diff==0):

row_cosines = lr_diff / np.sqrt(lr_diff.dot(lr_diff))

col_cosines = -si_diff / np.sqrt(si_diff.dot(si_diff))

else:

row_cosines = np.array([1.,0,0])

col_cosines = np.array([0,-1.,0])

self.slice_order = nimsmrdata.SLICE_ORDER_UNKNOWN

# FIXME: check that this is correct.

if self._hdr.series.se_sortorder == 0:

self.slice_order = nimsmrdata.SLICE_ORDER_SEQ_INC

elif self._hdr.series.se_sortorder == 1:

self.slice_order = nimsmrdata.SLICE_ORDER_ALT_INC

slice_norm = np.array([-self._hdr.image.norm_R, -self._hdr.image.norm_A, self._hdr.image.norm_S])

# This is either the first slice tlhc (image_tlhc) or the last slice tlhc. How to decide?

# And is it related to wheather I have to negate the slice_norm?

# Tuned this empirically by comparing spiral and EPI data with the same Rx.

# Everything seems reasonable, except the test for axial orientation (start_ras==S|I).

# I have no idea why I need that! But the flipping only seems necessary for axials, not

# coronals or the few obliques I've tested.

# FIXME: haven't tested sagittals!

if (self._hdr.series.start_ras=='S' or self._hdr.series.start_ras=='I') and self._hdr.series.start_loc > self._hdr.series.end_loc:

self.reverse_slice_order = True

slice_fov = np.abs(self._hdr.series.start_loc - self._hdr.series.end_loc)

image_position = image_tlhc - slice_norm * slice_fov

# FIXME: since we are reversing the slice order here, should we change the slice_order field below?

else:

image_position = image_tlhc

self.reverse_slice_order = False

if self.num_bands > 1:

image_position = image_position - slice_norm * self.band_spacing_mm * (self.num_bands - 1.0) / 2.0

#origin = image_position * np.array([-1, -1, 1])

# Fix the half-voxel offset. Apparently, the p-file convention specifies coords at the

# corner of a voxel. But DICOM/NIFTI convention is the voxel center. So offset by a half-voxel.

origin = image_position + (row_cosines+col_cosines)*(np.array(self.mm_per_vox)/2)

# The DICOM standard defines these two unit vectors in an LPS coordinate frame, but we'll

# need RAS (+x is right, +y is anterior, +z is superior) for NIFTI. So, we compute them

# such that self.row_cosines points to the right and self.col_cosines points up.

row_cosines[0:2] = -row_cosines[0:2]

col_cosines[0:2] = -col_cosines[0:2]

if self.is_dwi and self.dwi_bvalue==0:

log.warning('the data appear to be diffusion-weighted, but image.b_value is 0!')

# The bvals/bvecs will get set later

self.bvecs,self.bvals = (None,None)

self.image_rotation = nimsmrdata.compute_rotation(row_cosines, col_cosines, slice_norm)

self.qto_xyz = nimsmrdata.build_affine(self.image_rotation, self.mm_per_vox, origin)

self.scan_type = self.infer_scan_type()

self.aux_files = None

@property

def canonical_filename(self):

return self.pfilename

def get_bvecs_bvals(self):

tensor_file = os.path.join(self.dirpath, '_'+self.basename+'_tensor.dat')

if not os.path.exists(tensor_file):

log.warning('tensor file not found!')

self.bvecs = None

self.bvals = None

return

with open(tensor_file) as fp:

uid = fp.readline().rstrip()

ndirs = int('0'+fp.readline().rstrip())

bvecs = np.fromfile(fp, sep=' ')

if uid != self._hdr.series.series_uid:

raise NIMSPFileError('tensor file UID does not match PFile UID!')

if ndirs != self.dwi_numdirs or self.dwi_numdirs != bvecs.size / 3.:

log.warning('tensor file numdirs does not match PFile header numdirs!')

self.bvecs = None

self.bvals = None

else:

num_nondwi = self.num_timepoints_available - self.dwi_numdirs # FIXME: assumes that all the non-dwi images are acquired first.

bvals = np.concatenate((np.zeros(num_nondwi, dtype=float), np.tile(self.dwi_bvalue, self.dwi_numdirs)))

bvecs = np.hstack((np.zeros((3,num_nondwi), dtype=float), bvecs.reshape(self.dwi_numdirs, 3).T))

self.bvecs,self.bvals = nimsmrdata.adjust_bvecs(bvecs, bvals, self.scanner_type, self.image_rotation)

@property

def recon_func(self):

if self.psd_type == 'spiral':

return self.recon_spirec

elif self.psd_type == 'muxepi':

return self.recon_mux_epi

elif self.psd_type == 'mrs':

return self.recon_mrs

elif self.psd_type == 'hoshim':

return self.recon_hoshim

elif self.psd_type == 'basic':

return self.recon_basic

else:

return None

@property

def priority(self):

return int(bool(self.recon_func)) * 2 - 1 # return 1 if we can recon, else -1

def load_all_metadata(self):

if self.is_dwi:

self.get_bvecs_bvals()

super(NIMSPFile, self).load_all_metadata()

def prep_convert(self):

# FIXME: the following is a hack to get mux_epi2 SE-IR scans to recon properly. There *is* a more generic solution...

if self.psd_type=='muxepi' and (self.num_mux_cal_cycle<2 or (self.psd_name=='mux_epi2' and self.ti>0)):

# Mux scan without internal calibration-- request other mux scans be handed to convert

# to see if we can find a suitable calibration scan.

aux_data = { 'psd': self.psd_name }

else:

aux_data = None

return aux_data

def convert(self, outbase, tempdir=None, num_jobs=8, aux_files=None):

self.load_all_metadata()

self.aux_files = aux_files

if self.imagedata is None:

self.get_imagedata(tempdir, num_jobs)

result = (None, None)

if self.imagedata is not None: # catches, for example, HO Shims

if self.reverse_slice_order:

self.imagedata = self.imagedata[:,:,::-1,]

if self.fm_data is not None:

self.fm_data = self.fm_data[:,:,::-1,]

if self.psd_type=='spiral' and self.num_echos == 2:

# Uncomment to save spiral in/out

#nimsnifti.NIMSNifti.write(self, self.imagedata[:,:,:,:,0], outbase + '_in')

#nimsnifti.NIMSNifti.write(self, self.imagedata[:,:,:,:,1], outbase + '_out')

# FIXME: Do a more robust test for spiralio!

# Assume spiralio, so do a weighted average of the two echos.

# FIXME: should do a quick motion correction here

w_in = np.mean(self.imagedata[:,:,:,:,0], 3)

w_out = np.mean(self.imagedata[:,:,:,:,1], 3)

inout_sum = w_in + w_out

w_in = w_in / inout_sum

w_out = w_out / inout_sum

avg = np.zeros(self.imagedata.shape[0:4])

for tp in range(self.imagedata.shape[3]):

avg[:,:,:,tp] = w_in*self.imagedata[:,:,:,tp,0] + w_out*self.imagedata[:,:,:,tp,1]

result = ('nifti', nimsnifti.NIMSNifti.write(self, avg, outbase))

else:

result = ('nifti', nimsnifti.NIMSNifti.write(self, self.imagedata, outbase))

if self.fm_data is not None:

nimsnifti.NIMSNifti.write(self, self.fm_data, outbase + '_B0')

return result

def get_imagedata(self, tempdir, num_jobs):

if self.recon_func:

self.recon_func(tempdir=tempdir, num_jobs=num_jobs)

else:

raise NIMSPFileError('Recon not implemented for this type of data')

def load_imagedata_from_file(self, filepath):

""" Load raw image data from a file and do some sanity checking on num slices, matrix size, etc. """

# TODO: confirm that the voxel reordering is necessary. Maybe lean on the recon folks to standardize their voxel order?

import scipy.io

mat = scipy.io.loadmat(filepath)

if 'd' in mat:

sz = mat['d_size'].flatten().astype(int)

slice_locs = mat['sl_loc'].flatten().astype(int) - 1

imagedata = np.zeros(sz, mat['d'].dtype)

raw = np.atleast_3d(mat['d'])

imagedata[:,:,slice_locs,...] = raw[::-1,...]

elif 'MIP_res' in mat:

imagedata = np.atleast_3d(mat['MIP_res'])

imagedata = imagedata.transpose((1,0,2,3))[::-1,::-1,:,:]

if imagedata.ndim == 3:

imagedata = imagedata.reshape(imagedata.shape + (1,))

return imagedata

def update_imagedata(self, imagedata):

self.imagedata = imagedata

if self.imagedata.shape[0] != self.size[0] or self.imagedata.shape[1] != self.size[1]:

log.warning('Image matrix discrepancy. Fixing the header, assuming imagedata is correct...')

self.size = [self.imagedata.shape[0], self.imagedata.shape[1]]

self.mm_per_vox[0] = self.fov[0] / self.size[0]

self.mm_per_vox[1] = self.fov[1] / self.size[1]

if self.imagedata.shape[2] != self.num_slices * self.num_bands:

log.warning('Image slice count discrepancy. Fixing the header, assuming imagedata is correct...')

self.num_slices = self.imagedata.shape[2]

if self.imagedata.shape[3] != self.num_timepoints:

log.warning('Image time frame discrepancy (header=%d, array=%d). Fixing the header, assuming imagedata is correct...'

% (self.num_timepoints, self.imagedata.shape[3]))

self.num_timepoints = self.imagedata.shape[3]

self.duration = self.num_timepoints * self.tr # FIXME: maybe need self.num_echos?

def recon_hoshim(self, tempdir, num_jobs):

log.debug('Cannot recon HO SHIM data')

def recon_basic(self, tempdir, num_jobs):

log.debug('Cannot recon BASIC data')

def recon_spirec(self, tempdir, num_jobs):

"""Do spiral image reconstruction and populate self.imagedata."""

with tempfile.TemporaryDirectory(dir=tempdir) as temp_dirpath:

if self.compressed:

pfile_path = os.path.join(temp_dirpath, self.basename)

with open(pfile_path, 'wb') as fd:

with gzip.open(self.filepath, 'rb') as gzfile:

fd.writelines(gzfile)

else:

pfile_path = self.filepath

basepath = os.path.join(temp_dirpath, 'recon')

cmd = 'spirec -l --rotate -90 --magfile --savefmap2 --b0navigator -r %s -t %s' % (pfile_path, 'recon')

log.debug(cmd)

subprocess.call(shlex.split(cmd), cwd=temp_dirpath, stdout=open('/dev/null', 'w')) # run spirec to generate .mag and fieldmap files

self.imagedata = np.fromfile(file=basepath+'.mag_float', dtype=np.float32).reshape([self.size[0],self.size[1],self.num_timepoints,self.num_echos,self.num_slices],order='F').transpose((0,1,4,2,3))

if os.path.exists(basepath+'.B0freq2') and os.path.getsize(basepath+'.B0freq2')>0:

self.fm_data = np.fromfile(file=basepath+'.B0freq2', dtype=np.float32).reshape([self.size[0],self.size[1],self.num_echos,self.num_slices],order='F').transpose((0,1,3,2))

def find_mux_cal_file(self):

cal_file = []

if self.aux_files!=None and len(self.aux_files)>0 and self.aux_files[0]!=None:

if self.num_mux_cal_cycle>=2:

candidates = [pf for pf in [(NIMSPFile(f),f) for f in self.aux_files] if pf[0].num_bands==1]

else:

candidates = [pf for pf in [(NIMSPFile(f),f) for f in self.aux_files] if pf[0].num_mux_cal_cycle>=2]

if len(candidates)==1:

cal_file = candidates[0][1].encode()

elif len(candidates)>1:

series_num_diff = np.array([c[0].series_no for c in candidates]) - self.series_no

closest = np.min(np.abs(series_num_diff))==np.abs(series_num_diff)

# there may be more than one. We prefer the prior scan:

closest = np.where(np.min(series_num_diff[closest])==series_num_diff)[0][0]

cal_file = candidates[closest][1].encode()

if len(cal_file)>0:

cal_compressed = is_compressed(cal_file)

cal_basename = cal_file[:-3] if cal_compressed else cal_file

cal_ref_file = os.path.join(os.path.dirname(cal_basename), '_'+os.path.basename(cal_basename)+'_ref.dat')

cal_vrgf_file = os.path.join(os.path.dirname(cal_basename), '_'+os.path.basename(cal_basename)+'_vrgf.dat')

else:

cal_compressed = False

cal_ref_file = ''

cal_vrgf_file = ''

# Make sure we return an empty string when none is found.

if not cal_file:

cal_file = ''

return cal_file,cal_ref_file,cal_vrgf_file,cal_compressed

def recon_mux_epi(self, tempdir, num_jobs, timepoints=[], octave_bin='octave'):

start_sec = time.time()

"""Do mux_epi image reconstruction and populate self.imagedata."""

ref_file = os.path.join(self.dirpath, '_'+self.basename+'_ref.dat')

vrgf_file = os.path.join(self.dirpath, '_'+self.basename+'_vrgf.dat')

# See if external calibration data files are needed:

cal_file,cal_ref_file,cal_vrgf_file,cal_compressed = self.find_mux_cal_file()

# The dat files might be missing or empty if the vendor recon was disabled. If so, try to use the cal dat file.

# FIXME: if the p-file is not compressed, the cal dat file will not be used! We should refactor the recon

# code so that the dat files are always explicitly specified.

if not os.path.isfile(ref_file) or os.path.getsize(ref_file)<64:

if cal_ref_file:

ref_file = cal_ref_file

else:

raise NIMSPFileError('ref.dat file not found')

if not os.path.isfile(vrgf_file) or os.path.getsize(vrgf_file)<64:

if cal_vrgf_file:

vrgf_file = cal_vrgf_file

else:

raise NIMSPFileError('vrgf.dat file not found')

# HACK to force SENSE recon for caipi data

#sense_recon = 1 if 'CAIPI' in self.series_desc else 0

sense_recon = 0

fermi_filt = 1

notch_thresh = 0

with tempfile.TemporaryDirectory(dir=tempdir) as temp_dirpath:

log.info('Running %d v-coil mux recon on %s in tempdir %s with %d jobs (sense=%d, fermi=%d, notch=%f).'

% (self.num_vcoils, self.filepath, tempdir, num_jobs, sense_recon, fermi_filt, notch_thresh))

if cal_file!='':

log.info('Using calibration file: %s.' % cal_file)

if self.compressed:

shutil.copy(ref_file, os.path.join(temp_dirpath, os.path.basename(ref_file)))

shutil.copy(vrgf_file, os.path.join(temp_dirpath, os.path.basename(vrgf_file)))

pfile_path = uncompress(self.filepath, temp_dirpath)

else:

pfile_path = self.filepath

if cal_file and cal_compressed:

shutil.copy(cal_ref_file, os.path.join(temp_dirpath, os.path.basename(cal_ref_file)))

shutil.copy(vrgf_file, os.path.join(temp_dirpath, os.path.basename(cal_vrgf_file)))

cal_file = uncompress(cal_file, temp_dirpath)

recon_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'mux_epi_recon'))

outname = os.path.join(temp_dirpath, 'sl')

# Spawn the desired number of subprocesses until all slices have been spawned

mux_recon_jobs = []

slice_num = 0

while slice_num < self.num_slices:

num_running_jobs = sum([job.poll()==None for job in mux_recon_jobs])

if num_running_jobs < num_jobs:

# Recon each slice separately. Note the slice_num+1 to deal with matlab's 1-indexing.

# Use 'str' on timepoints so that an empty array will produce '[]'

cmd = ('%s --no-window-system -p %s --eval \'mux_epi_main("%s", "%s_%03d.mat", "%s", %d, %s, %d, 0, %s, %s, %s);\''

% (octave_bin, recon_path, pfile_path, outname, slice_num, cal_file, slice_num + 1, str(timepoints), self.num_vcoils, str(sense_recon), str(fermi_filt), str(notch_thresh)))

log.debug(cmd)

mux_recon_jobs.append(subprocess.Popen(args=shlex.split(cmd), stdout=open('/dev/null', 'w')))

slice_num += 1

else:

time.sleep(1.)

# Now wait for all the jobs to finish

for job in mux_recon_jobs:

job.wait()

# Load the first slice to initialize the image array

img = self.load_imagedata_from_file("%s_%03d.mat" % (outname, 0))

for slice_num in range(1, self.num_slices):

new_img = self.load_imagedata_from_file("%s_%03d.mat" % (outname, slice_num))

# Allow for a partial last timepoint. This sometimes happens when the user aborts.

t = min(img.shape[-1], new_img.shape[-1])

img[...,0:t] += new_img[...,0:t]

img = img.astype(np.float32)

self.update_imagedata(img)

elapsed = time.time() - start_sec

log.info('Mux recon of %s with %d v-coils finished in %0.2f minutes using %d jobs.'

% (self.filepath, self.num_vcoils, elapsed/60., min(num_jobs, self.num_slices)))

def recon_mrs(self, tempdir, num_jobs):

"""Currently just loads raw spectro data into self.imagedata so that we can save it in a nifti."""

# Reorder the data to be in [frame, num_frames, slices, passes (repeats), echos, coils]

# This roughly complies with the nifti standard of x,y,z,time,[then whatever].

# Note that the "frame" is the line of k-space and thus the FID timeseries.

self.imagedata = self.get_rawdata().transpose([0,5,3,1,2,4])

def get_rawdata(self, slices=None, passes=None, coils=None, echos=None, frames=None):

"""

Reads and returns a chunck of data from the p-file. Specify the slices,

timepoints, coils, and echos that you want. None means you get all of

them. The default of all Nones will return all data.

(based on https://github.com/cni/MRS/blob/master/MRS/files.py)

"""

n_frames = self._hdr.rec.nframes + self._hdr.rec.hnover

n_echos = self._hdr.rec.nechoes

n_slices = self._hdr.rec.nslices / self._hdr.rec.npasses

n_coils = self.num_receivers

n_passes = self._hdr.rec.npasses

frame_sz = self._hdr.rec.frame_size

if passes == None: passes = range(n_passes)

if coils == None: coils = range(n_coils)

if slices == None: slices = range(n_slices)

if echos == None: echos = range(n_echos)

if frames == None: frames = range(n_frames)

# Size (in bytes) of each sample:

ptsize = self._hdr.rec.point_size

data_type = [np.int16, np.int32][ptsize/2 - 1]

# This is double the size as above, because the data is complex:

frame_bytes = 2 * ptsize * frame_sz

echosz = frame_bytes * (1 + n_frames)

slicesz = echosz * n_echos

coilsz = slicesz * n_slices

passsz = coilsz * n_coils

# Byte-offset to get to the data:

offset = self._hdr.rec.off_data

fp = gzip.open(self.filepath, 'rb') if self.compressed else open(self.filepath, 'rb')

data = np.zeros((frame_sz, len(frames), len(echos), len(slices), len(coils), len(passes)), dtype=np.complex)

for pi,passidx in enumerate(passes):

for ci,coilidx in enumerate(coils):

for si,sliceidx in enumerate(slices):

for ei,echoidx in enumerate(echos):

for fi,frameidx in enumerate(frames):

fp.seek(passidx*passsz + coilidx*coilsz + sliceidx*slicesz + echoidx*echosz + (frameidx+1)*frame_bytes + offset)

# Unfortunately, numpy fromfile doesn't like gzip file objects. But we can

# safely load each chunk into RAM, since frame_sz is never very big.

#dr = np.fromfile(fp, data_type, frame_sz * 2)

dr = np.fromstring(fp.read(frame_bytes), data_type)

dr = np.reshape(dr, (-1, 2)).T

data[:, fi, ei, si, ci, pi] = dr[0] + dr[1]*1j

fp.close()

def __init__(self):

super(ArgumentParser, self).__init__()

self.description = """Recons a GE PFile to produce a NIfTI file and (if appropriate) a B0 fieldmap.

Can also take the image data in a .mat file, in which case no recon will be attempted

and the PFile is just used to get the necessary header information."""

self.add_argument('pfile', help='path to pfile')

self.add_argument('outbase', nargs='?', help='basename for output files (default: [pfile_name].nii.gz in cwd)')

self.add_argument('-m', '--matfile', help='path to reconstructed data in .mat format')

self.add_argument('-t', '--tempdir', help='directory to use for scratch files (must exist and have lots of space!)')

self.add_argument('-j', '--jobs', default=8, type=int, help='maximum number of processes to spawn')

self.add_argument('-v', '--vcoils', default=0, type=int, help='number of virtual coils (0=all)')