Output to .txt and format numbers with commas

README.md

@@ -43,6 +43,28 @@ mv brain_recon.nii.gz incoming/
 singularity exec docker://fnndsc/pl-mri-preview mri_preview incoming/ outgoing/
 ```
 
+## File Types
+
+### `--input-suffix`
+
+Every input file with a file name ending with a value given by `--input-suffix`
+is processed. Unmatched files are ignored. Supported formats are listed on
+[NiBabel](https://nipy.org/nibabel/)'s website.
+
+### `--output`
+
+`pl-mri-preview` creates image files. Supported output formats are
+any which are supported by
+[matplotlib](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html),
+including `.png`, `.jpg`, and `.svg`.
+
+The special type `.txt` writes a plaintext file instead, e.g.
+
+```
+2236612 voxels
+230437.66977741406 mm^3
+```
+
 ## About Brain Volume
 
 ### Input File

examples/out/sub-feta037_T2w.nii.txt

@@ -0,0 +1,2 @@
+2236612 voxels
+230437.66977741406 mm^3

mri_preview.py

@@ -4,6 +4,7 @@
 from pathlib import Path
 from argparse import ArgumentParser, Namespace, ArgumentDefaultsHelpFormatter
 from importlib.metadata import Distribution
+from typing import Iterable
 
 import nibabel as nib
 import numpy.typing as npt
@@ -35,25 +36,25 @@
  formatter_class=ArgumentDefaultsHelpFormatter)
 parser.add_argument('-b', '--background', default=0.0, type=float,
  help='threshold indicating background voxels')
-parser.add_argument('-p', '--pattern', default='**/*.nii*',
- help='input file pattern')
-parser.add_argument('-o', '--output', default='.png',
- help='output file extension')
+parser.add_argument('-i', '--inputs', default='.nii,.nii.gz,.mnc,.mgz',
+ help='file extension of input files, comma-separated')
+parser.add_argument('-o', '--outputs', default='.png,.txt',
+ help='output file extensions, comma-separated')
 parser.add_argument('-V', '--version', action='version',
  version=f'$(prog)s {__version__}')
 
 
-def total_volume(img, threshold: float = 0.0) -> tuple[int, str]:
+def total_volume(img, threshold: float = 0.0) -> tuple[int, float, str]:
  """
  :param img: nibabel image
  :param threshold: foreground intensity threshold
- :return: total number of voxels and volume with units
+ :return: total number of voxels, volume, and cubic units of the volume
  """
  data = img.get_fdata()
  units, _ = img.header.get_xyzt_units()
  num_voxels = count_positive(data, threshold)
  total_vol = num_voxels * get_voxel_size(img.affine)
- return num_voxels, f'{total_vol:.3f} {units}\u00B3'
+ return num_voxels, total_vol, units
 
 
 def get_voxel_size(affine: npt.NDArray) -> float:
@@ -98,6 +99,24 @@ def slices_figure(data: npt.NDArray, caption: str) -> plt.Figure:
  return fig
 
 
+def multi_mapper(inputdir: Path, outputdir: Path, file_extensions: str) -> Iterable[tuple[Path, Path]]:
+ for file_extension in file_extensions.split(','):
+ yield from PathMapper(
+ inputdir, outputdir,
+ glob=f'**/*{file_extension}', suffix='.out', fail_if_empty=False
+ )
+
+
+def save_as(img, output: Path, num_voxels: int, total_vol: float, units: str) -> None:
+ if output.name.endswith('.txt'):
+ with output.open('w') as f:
+ f.write(f'{num_voxels} voxels\n{total_vol} {units}^3')
+ else:
+ text = f'total volume = \n{num_voxels:,} voxels\n{total_vol:,.1f} {units}\u00B3'
+ fig = slices_figure(img.get_fdata(), text)
+ fig.savefig(output)
+
+
 @chris_plugin(
  parser=parser,
  title='Brain Volume',
@@ -107,19 +126,21 @@ def slices_figure(data: npt.NDArray, caption: str) -> plt.Figure:
 )
 def main(options: Namespace, inputdir: Path, outputdir: Path):
  print(DISPLAY_TITLE, file=sys.stderr, flush=True)
- logger.debug('input files pattern: "{}"', options.pattern)
+ logger.debug('input files: {}', options.inputs.split(','))
+ logger.debug('output formats: {}', options.outputs.split(','))
  logger.debug('background threshold: {}', options.background)
- mapper = PathMapper(inputdir, outputdir, glob=options.pattern, suffix=options.output)
+ mapper = multi_mapper(inputdir, outputdir, options.inputs)
 
- for input_file, output_file in mapper:
+ for input_file, output_base in mapper:
  try:
  img = nib.load(input_file)
- data = img.get_fdata()
- num_voxels, total_vol = total_volume(img, options.background)
- text = f'total volume = \n{num_voxels} voxels\n{total_vol}'
- fig = slices_figure(data, text)
- fig.savefig(output_file)
- logger.info('{} -> {}: {} voxels, volume={}', input_file, output_file, num_voxels, total_vol)
+ num_voxels, total_vol, units = total_volume(img, options.background)
+
+ logger.info('{}: {} voxels, volume={} {}^3', input_file, num_voxels, total_vol, units)
+ for output_ext in options.outputs.split(','):
+ output_file = output_base.with_suffix(output_ext)
+ save_as(img, output_file, num_voxels, total_vol, units)
+ logger.info('\t-> {}', output_file)
  except Exception:
  logger.error('Failed to process {}', input_file)
  raise

requirements.txt

@@ -1,4 +1,4 @@
-chris_plugin~=0.0.14
+chris_plugin~=0.0.15
 nibabel~=3.2.2
 matplotlib~=3.5.1
 loguru~=0.6.0

setup.py

@@ -2,7 +2,7 @@
 
 setup(
  name='mri-preview',
- version='1.0.0',
+ version='1.1.0',
  description='A ChRIS plugin to preview the center slices of MRI',
  author='Jennings Zhang',
  author_email='Jennings.Zhang@childrens.harvard.edu',