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nisnap

Project description

nisnap

pipeline status coverage report downloads python versions pypi version

Create snapshots of segmentation maps produced by neuroimaging software. Inspired by tools like nilearn, visualqc, fmriprep and others.

example

example

Usage

From a Terminal:

nisnap c1.nii.gz c2.nii.gz c3.nii.gz --bg /tmp/raw.nii.gz --opacity 50 -o /tmp/snapshot.gif

nisnap labels.nii.gz --bg raw.nii.gz --opacity 50 --axes x --contours -o /tmp/snapshot.gif
Arguments:

  files                 segmentation map(s) to create snapshots from

optional arguments:
  --bg BG               background image on which segmentations will be plotted.
  --axes AXES           choose the direction of the cuts (among 'x', 'y', or 'z')
  --opacity OPACITY     opacity (in %) of the segmentation maps when plotted over a background image. Only used if a background image is provided.
  --contours            if True, segmentations will be rendered as contoured regions. If False, will be rendered as superimposed masks.
  -o OUTPUT, --output OUTPUT
                        snapshot will be stored in this file. If extension is .gif, snapshot will be rendered as an animation.
  --config CONFIG       [XNAT mode] XNAT configuration file
  --nobg                [XNAT mode] no background image. Plots segmentation maps only.
  -e EXPERIMENT, --experiment EXPERIMENT
                        [XNAT mode] ID of the experiment to create snapshots from.
  --resource RESOURCE   [XNAT mode] name of the resource to download
  --cache               [XNAT mode] skip downloads (e.g. if running for a second time
  --disable_warnings
  --verbose

From IPython/Jupyter Notebook:

Example:

import nisnap
filepaths = ['c1.nii.gz', 'c2.nii.gz', 'c3.nii.gz']
bg = 'source.nii.gz'
nisnap.plot_segment(filepaths, bg=bg, opacity=30, axes='x', animated=True)

Reference:

def plot_segment(filepaths, axes='xyz', bg=None, opacity=30, slices=None,
        animated=False, savefig=None, contours=False, rowsize=None,
        figsize=None, width=2000):
    """Plots a set of segmentation maps/masks.

    Parameters
    ----------
    filepaths: a list of str
        Paths to segmentation maps (between 1 and 3). Must be of same dimensions
        and in same reference space.

    axes: string, or a tuple of strings
        Choose the direction of the cuts (among 'x', 'y', or 'z')

    bg: None or str
        Path to the background image that the masks will be plotted on top of.
        If nothing is specified, the segmentation maps/masks will be plotted only.
        The opacity (in %) of the segmentation maps when plotted over a background
        image. Only used if a background image is provided. Default: 10

    slices: None, or a tuple of floats
        The indexes of the slices that will be rendered. If None is given, the
        slices are selected automatically.

    animated: boolean, optional
        If True, the snapshot will be rendered as an animated GIF.
        If False, the snapshot will be rendered as a static PNG image. Default:
        False

    savefig: string, optional
        Filepath where the resulting snapshot will be created. If None is given,
        a temporary file will be created and/or the result will be displayed
        inline in a Jupyter Notebook.

    contours: boolean, optional
        If True, segmentations will be rendered as contoured regions. If False,
        will be rendered as superimposed masks. Default: False

    rowsize: None, or int, or dict
        Set the number of slices per row in the final compiled figure.
        Default: {'x': 9, 'y': 9, 'z': 6}

    figsize: None, or a 2-uple of floats, or dict
        Sets the dimensions of one row of slices.
        Default: {'x': (37, 3), 'y': (40, 3), 'z': (18, 3)}

    width: int, optional
        Width (in px) of the final compiled figure. Default: 2000.


    See Also
    --------
    xnat.plot_segment : To plot segmentation maps directly providing their
        experiment_id on an XNAT instance
    """

Using XNAT

From a Terminal:

nisnap --config .xnat.cfg -e EXPERIMENT_ID --resource ASHS --axes A --opacity 50 -o /tmp/test.gif

From IPython/Jupyter Notebook:

Example:

from nisnap import xnat
xnat.plot_segment(config='/home/grg/.xnat.cfg', experiment_id='BBRC_E000',
  raw=True, opacity=30, axes='x', slices=range(100,120,2), figsize=(15,5),
  animated=True)

Reference:

def plot_segment(config, experiment_id, savefig=None, slices=None,
    resource_name='SPM12_SEGMENT_T2T1_COREG',
    axes='xyz', raw=True, opacity=10, animated=False, rowsize=None,
    figsize=None, width=2000, contours=False, cache=False):
    """Download a given experiment/resource from an XNAT instance and create
    snapshots of this resource along a selected set of slices.

    Parameters
    ----------
    config: string
        Configuration file to the XNAT instance.

    experiment_id : string
        ID of the experiment from which to download the segmentation maps and
        raw anatomical image.

    savefig: string, optional
        Filepath where the resulting snapshot will be created. If None is given,
        a temporary file will be created and/or the result will be displayed
        inline in a Jupyter Notebook.

    slices: None, or a tuple of floats
        The indexes of the slices that will be rendered. If None is given, the
        slices are selected automatically.

    resource_name: string, optional
        Name of the resource where the segmentation maps are stored in the XNAT
        instance. Default: SPM12_SEGMENT_T2T1_COREG

    axes: string, or a tuple of strings
        Choose the direction of the cuts (among 'x', 'y', 'z')

    raw: boolean, optional
        If True, the segmentation maps will be plotted over a background image
        (e.g. anatomical T1 or T2, as in xnat.download_resources). If False,
        the segmentation maps will be rendered only. Default: True

    opacity: integer, optional
        The opacity (in %) of the segmentation maps when plotted over a background
        image. Only used if a background image is provided. Default: 10

    animated: boolean, optional
        If True, the snapshot will be rendered as an animated GIF.
        If False, the snapshot will be rendered as a static PNG image. Default:
        False

    rowsize: None, or int, or dict
        Set the number of slices per row in the final compiled figure.
        Default: {'x': 9, 'y': 9, 'z': 6}

    figsize: None, or a 2-uple of floats, or dict
        Sets the dimensions of one row of slices.
        Default: {'x': (37, 3), 'y': (40, 3), 'z': (18, 3)}

    width: int, optional
        Width (in px) of the final compiled figure. Default: 2000.

    contours: boolean, optional
        If True, segmentations will be rendered as contoured regions. If False,
        will be rendered as superimposed masks. Default: False

    cache: boolean, optional
        If False, resources will be normally downloaded from XNAT. If True,
        download will be skipped and data will be looked up locally.
        Default: False

    Notes
    -----
    Requires an XNAT instance where SPM segmentation maps will be found
    following a certain data organization in experiment resources named
    `resource_name`.

    See Also
    --------
    xnat.download_resources : To download resources (e.g. segmentation maps +
        raw images) from an XNAT instance (e.g. prior to snapshot creation)
    nisnap.plot_segment : To plot segmentation maps directly providing their
        filepaths
    """
def download_resources(config, experiment_id, resource_name,  destination,
    raw=True, cache=False):
    """Download a given experiment/resource from an XNAT instance in a local
    destination folder.

    Parameters
    ----------
    config: string
        Configuration file to the XNAT instance.
        See http://xgrg.github.io/first-steps-with-pyxnat/ for more details.

    experiment_id : string
        ID of the experiment from which to download the segmentation maps and
        raw anatomical image.

    resource_name: string
        Name of the resource where the segmentation maps are stored in the XNAT
        instance.

    destination: string
        Destination folder where to store the downloaded resources.

    raw: boolean, optional
        If True, a raw anatomical image will be downloaded along with the
        target resources. If False, only the resources referred to by
        `resource_name` will be downloaded. Default: True

    cache: boolean, optional
        If False, resources will be normally downloaded from XNAT. If True,
        download will be skipped and data will be looked up locally.
        Default: False

    Notes
    -----
    Requires an XNAT instance where SPM segmentation maps will be found
    following a certain data organization in experiment resources named
    `resource_name`.

    See Also
    --------
    xnat.plot_segment : To plot segmentation maps directly providing their
        experiment_id on an XNAT instance
    nisnap.plot_segment : To plot segmentation maps directly providing their
        filepaths
    """

How to install

pip install nisnap

Credits

Greg Operto and Jordi Huguet (BarcelonaBeta Brain Research Center)

Project details


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