BF: parrec2nii does not fail immediately if one conversions fails. closes gh-30

Instead it keeps processing files and collects errors. At the end it
will dump all exceptions and fail with non-zero exit code.

Author: mih

bin/parrec2nii

@@ -81,6 +81,106 @@ def error(msg, exit_code):
     print msg
     sys.exit(exit_code)
 
+def proc_file(infile, opts):
+    # load the PAR header
+    pr_img = pr.load(infile)
+    pr_hdr = pr_img.get_header()
+    # get the raw unscaled data form the REC file
+    raw_data = read_img_data(pr_img, prefer='unscaled')
+
+    # compute affine with desired origin
+    affine = pr_hdr.get_affine(origin=opts.origin)
+
+    # create an nifti image instance -- to get a matching header
+    nimg = nifti1.Nifti1Image(raw_data, affine)
+    nhdr = nimg.get_header()
+
+    if 'parse' in opts.minmax:
+        # need to get the scaled data
+        verbose('Load (and scale) the data to determine value range')
+        if opts.scaling == 'off':
+            scaled_data = raw_data
+        else:
+            slope, intercept = pr_hdr.get_data_scaling(method=opts.scaling)
+            scaled_data = slope * raw_data
+            scaled_data += intercept
+    if opts.minmax[0] == 'parse':
+        nhdr.structarr['cal_min'] = scaled_data.min()
+    else:
+        nhdr.structarr['cal_min'] = float(opts.minmax[0])
+    if opts.minmax[1] == 'parse':
+        nhdr.structarr['cal_max'] = scaled_data.max()
+    else:
+        nhdr.structarr['cal_max'] = float(opts.minmax[1])
+
+    # container for potential NIfTI1 header extensions
+    exts = nifti1.Nifti1Extensions()
+
+    if opts.store_header:
+        # dump the full PAR header content into an extension
+        fobj = open(infile, 'r')
+        hdr_dump = fobj.read()
+        dump_ext = nifti1.Nifti1Extension('comment', hdr_dump)
+        fobj.close()
+        exts.append(dump_ext)
+
+    # put any extensions into the image
+    nimg.extra['extensions'] = exts
+
+    # image description
+    descr = "%s;%s;%s;%s" % (
+                pr_hdr.general_info['exam_name'],
+                pr_hdr.general_info['patient_name'],
+                pr_hdr.general_info['exam_date'].replace(' ',''),
+                pr_hdr.general_info['protocol_name'])
+    nhdr.structarr['descrip'] = descr[:80]
+
+    if pr_hdr.general_info['max_dynamics'] > 1:
+        # fMRI
+        nhdr.structarr['pixdim'][4] = pr_hdr.general_info['repetition_time']
+        # store units -- always mm and msec
+        nhdr.set_xyzt_units('mm', 'msec')
+    else:
+        # anatomical or DTI
+        nhdr.set_xyzt_units('mm', 'unknown')
+
+    # get original scaling
+    if opts.scaling == 'off':
+        slope = 1.0
+        intercept = 0.0
+    else:
+        slope, intercept = pr_hdr.get_data_scaling(method=opts.scaling)
+        nhdr.set_slope_inter(slope, intercept)
+
+    # finalize the header: set proper data offset, pixdims, ...
+    nimg.update_header()
+
+    # figure out the output filename
+    outfilename = splitext_addext(os.path.basename(infile))[0]
+    if not opts.outdir is None:
+        # set output path
+        outfilename = os.path.join(opts.outdir, outfilename)
+
+    # prep a file
+    if opts.compressed:
+        verbose('Using gzip compression')
+        outfilename += '.nii.gz'
+        outfile = gzip.open(outfilename, 'w')
+    else:
+        outfilename += '.nii'
+        outfile = open(outfilename, 'w')
+
+    verbose('Writing %s' % outfilename)
+    # first write the header
+    nimg._write_header(outfile, nhdr, slope, intercept)
+    # now the data itself, but prevent any casting or scaling
+    nibabel.volumeutils.array_to_file(
+            raw_data,
+            outfile,
+            offset=nhdr.get_data_offset())
+    # done
+    outfile.close()
+
 
 def main():
     parser = get_opt_parser()
@@ -92,107 +192,20 @@ def main():
     if not opts.origin in ['scanner', 'fov']:
         error("Unrecognized value for --origin: '%s'." % opts.origin, 1)
 
+    # store any exceptions
+    errs = []
     for infile in infiles:
         verbose('Processing %s' % infile)
-        # load the PAR header
-        pr_img = pr.load(infile)
-        pr_hdr = pr_img.get_header()
-        # get the raw unscaled data form the REC file
-        raw_data = read_img_data(pr_img, prefer='unscaled')
-
-        # compute affine with desired origin
-        affine = pr_hdr.get_affine(origin=opts.origin)
-
-        # create an nifti image instance -- to get a matching header
-        nimg = nifti1.Nifti1Image(raw_data, affine)
-        nhdr = nimg.get_header()
-
-        if 'parse' in opts.minmax:
-            # need to get the scaled data
-            verbose('Load (and scale) the data to determine value range')
-            if opts.scaling == 'off':
-                scaled_data = raw_data
-            else:
-                slope, intercept = pr_hdr.get_data_scaling(method=opts.scaling)
-                scaled_data = slope * raw_data
-                scaled_data += intercept
-        if opts.minmax[0] == 'parse':
-            nhdr.structarr['cal_min'] = scaled_data.min()
-        else:
-            nhdr.structarr['cal_min'] = float(opts.minmax[0])
-        if opts.minmax[1] == 'parse':
-            nhdr.structarr['cal_max'] = scaled_data.max()
-        else:
-            nhdr.structarr['cal_max'] = float(opts.minmax[1])
-
-        # container for potential NIfTI1 header extensions
-        exts = nifti1.Nifti1Extensions()
-
-        if opts.store_header:
-            # dump the full PAR header content into an extension
-            fobj = open(infile, 'r')
-            hdr_dump = fobj.read()
-            dump_ext = nifti1.Nifti1Extension('comment', hdr_dump)
-            fobj.close()
-            exts.append(dump_ext)
-
-        # put any extensions into the image
-        nimg.extra['extensions'] = exts
-
-        # image description
-        descr = "%s;%s;%s;%s" % (
-                    pr_hdr.general_info['exam_name'],
-                    pr_hdr.general_info['patient_name'],
-                    pr_hdr.general_info['exam_date'].replace(' ',''),
-                    pr_hdr.general_info['protocol_name'])
-        nhdr.structarr['descrip'] = descr[:80]
-
-        if pr_hdr.general_info['max_dynamics'] > 1:
-            # fMRI
-            nhdr.structarr['pixdim'][4] = pr_hdr.general_info['repetition_time']
-            # store units -- always mm and msec
-            nhdr.set_xyzt_units('mm', 'msec')
-        else:
-            # anatomical or DTI
-            nhdr.set_xyzt_units('mm', 'unknown')
-
-        # get original scaling
-        if opts.scaling == 'off':
-            slope = 1.0
-            intercept = 0.0
-        else:
-            slope, intercept = pr_hdr.get_data_scaling(method=opts.scaling)
-            nhdr.set_slope_inter(slope, intercept)
-
-        # finalize the header: set proper data offset, pixdims, ...
-        nimg.update_header()
-
-        # figure out the output filename
-        outfilename = splitext_addext(os.path.basename(infile))[0]
-        if not opts.outdir is None:
-            # set output path
-            outfilename = os.path.join(opts.outdir, outfilename)
-
-        # prep a file
-        if opts.compressed:
-            verbose('Using gzip compression')
-            outfilename += '.nii.gz'
-            outfile = gzip.open(outfilename, 'w')
-        else:
-            outfilename += '.nii'
-            outfile = open(outfilename, 'w')
-
-        verbose('Writing %s' % outfilename)
-        # first write the header
-        nimg._write_header(outfile, nhdr, slope, intercept)
-        # now the data itself, but prevent any casting or scaling
-        nibabel.volumeutils.array_to_file(
-                raw_data,
-                outfile,
-                offset=nhdr.get_data_offset())
-        # done
-        outfile.close()
-
+        try:
+            proc_file(infile, opts)
+        except Exception as err:
+            errs.append('%s: %s' % (infile, err))
+
+    if len(errs):
+        error('Caught %i exceptions. Dump follows:\n\n %s'
+                % (len(errs), '\n'.join(errs)), 1)
+    else:
+        verbose('Done')
 
 
 if __name__ == '__main__':