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pycivet

.github/workflows/test.yml PyPI License - MIT

Python bindings for CIVET binaries like transform_objects and mincdefrag.

Abstract

pycivet is a helper library which provides a Python API that wraps CIVET binaries with object-oriented syntax. Intermediate files are written to temporary locations and cached.

Examples

Consider this bash script:

temp1=$(mktemp --suffix=.mnc)
temp2=$(mktemp)
mincresample -quiet -double mask.mnc $temp1
mincblur -quiet -fwhm $temp1 $temp2
mv "${temp2}_blur.mnc" blurred_mask.mnc
rm $temp1

The equivalent using pycivet:

from civet.minc import Mask
Mask("wm.right.mnc").resamplef64().mincblur(fwhm=3).save("blurred_mask.mnc")

This Perl code snippet from marching_cubes.pl can be expressed in Python as such:

https://github.com/aces/surface-extraction/blob/7c9c5987a2f8f5fdeb8d3fd15f2f9b636401d9a1/scripts/marching_cubes.pl.in#L125-L134

from civet import starting_models
starting_models.WHITE_MODEL_320.flip_x().slide_right().save('./output.obj')

Installation

It is recommended you install this package in a container image, e.g.

FROM docker.io/fnndsc/mni-conda-base:civet2.1.1-python3.10.2
RUN pip install pycivet

Motivation

Typically, bioinformatics and neuroinformatics pipelines such as CIVET and FreeSurfer are comprised of many binary programs and a script in csh or perl which glues together those binary programs and their intermediate results. These scripts look something like:

do_something input.mnc /tmp/1.mnc
another_thing /tmp/1.mnc /tmp/2.mnc
create_thing /tmp/3.mnc
many_thing /tmp/2.mnc /tmp/3.mnc /tmp/4.mnc
...

We propose that the readability and maintainability of such scripts can be improved using modern programming language features such as type hints. These advantages would enable to faster development and with fewer bugs. pycivet explores this concept with CIVET subroutines.

Features

pycivet is an object-oriented Python API to CIVET binaries.

Intermediate Files

Intermediate files are used to pass results between subroutines. This chore is handled transparently by the pycivet.memoization submodule.

Consider this excerpt from marching_cubes.pl:

&run( "param2xfm", "-scales", -1, 1, 1,
    "${tmpdir}/flip.xfm" );
&run( "transform_objects", $ICBM_white_model,
    "${tmpdir}/flip.xfm", $initial_model );
unlink( "${tmpdir}/flip.xfm" );
&run( "param2xfm", "-translation", 25, 0, 0,
    "${tmpdir}/slide_right.xfm" );
&run( "transform_objects", $initial_model,
    "${tmpdir}/slide_right.xfm", $initial_model );
unlink( "${tmpdir}/slide_right.xfm" );

Using pycivet we can express the code more concisely:

from civet.obj import Surface
Surface('input.obj').flip_x().translate_x(25).save('./output.obj')

Memoization

Repeated calls on the same object are cached. This is primarily for the sake of internal code quality, but it can also be taken advantage of externally:

from civet.memoization import Session
from civet.obj import Surface

with Session() as s:
    surf = Surface('input.obj')
    s.save(surf.flip_x(), 'flipped.obj')
    s.save(surf.flip_x().slide_right(), 'flipped_and_slid.obj')

In the example above, the following subroutine commands are cached:

  • param2xfm -scales -1 1 1 flip.xfm
  • transform_objects input.obj flip.xfm flipped.obj

Laziness

Only results which are needed (by save) are computed.

from civet.obj import Surface
surf = Surface('input.obj')
surf.slide_right()  # does nothing
surf.slide_left().save('left.obj')  # runs param2xfm, transform_objects, ...

Typing

Only methods relevant to an object's type are available to be called on that object. For instance, an object representing a .obj surface file would have the methods flip_x() and translate_x(n), and an object representing a .mnc volume would have the methods minccalc_u8(...) and mincdefrag(...) defined, but you cannot call Surface('input.obj').mincdefrag(1, 19). Subroutines and their usage are discoverable through autocomplete features of an IDE that supports type-hints.