Python API Reference¶

These are the classes and functions in pycortex.

Most commonly used modules
- quickflat
- webgl
- dataset
All the other modules
- align
- anat
- database
- freesurfer
- mapper
- mni
- polyutils
- segment
- surfinfo
- utils
- volume
- xfm

Most commonly used modules ¶

quickflat ¶

`make_figure`(braindata[, recache, pixelwise, …])	Show a Volume or Vertex on a flatmap with matplotlib.
`make_png`(fname, braindata[, recache, …])	Create a PNG of the VertexData or VolumeData on a flatmap.
`make_svg`(fname, braindata[, with_labels, …])	Save an svg file of the desired flatmap.

webgl ¶

Makes an interactive viewer for viewing data in a browser

`show`(data[, types, recache, cmap, layout, …])	Creates a webGL MRI viewer that is dynamically served by a tornado server running inside the current python process.
`make_static`(outpath, data[, types, recache, …])	Creates a static webGL MRI viewer in your filesystem so that it can easily be posted publically for sharing or just saved for later viewing.

dataset ¶

Contains classes for representing brain data in either volumetric or vertex (surface-based) formats for visualization.

`Volume`(data, subject, xfmname[, mask, cmap, …])	Encapsulates a 3D volume or 4D volumetric movie.
`Volume2D`(dim1, dim2[, subject, xfmname, …])	Contains two 3D volumes for simultaneous visualization.
`VolumeRGB`(channel1, channel2, channel3[, …])	Contains RGB (or RGBA) colors for each voxel in a volumetric dataset.
`Vertex`(data, subject[, cmap, vmin, vmax, …])	Encapsulates a 1D vertex map or 2D vertex movie.
`Vertex2D`(dim1, dim2[, subject, description, …])	Contains two vertex maps for simultaneous visualization.
`VertexRGB`(red, green, blue[, subject, …])	Contains RGB (or RGBA) colors for each vertex in a surface dataset.
`Dataset`(**kwargs)	Wrapper for multiple data objects.

All the other modules ¶

align ¶

Contains functions for making alignments between functional data and the surface, or, finding where the brain is.

`manual`(subject, xfmname[, reference])	Open GUI for manually aligning a functional volume to the cortical surface for subject.
`automatic`(subject, xfmname, reference[, …])	Create an automatic alignment using the FLIRT boundary-based alignment (BBR) from FSL.
`autotweak`(subject, xfmname)	Tweak an alignment using the FLIRT boundary-based alignment (BBR) from FSL.

anat ¶

Contains functions for making a whitematter mask

`brainmask`(outfile, subject)
`whitematter`(outfile, subject[, do_voxelize])
`voxelize`(outfile, subject[, surf, mp])	Voxelize the whitematter surface to generate the white matter mask

database ¶

Contains a singleton object db of type Database which allows easy access to surface files, anatomical images, and transforms that are stored in the pycortex filestore.

Database()

Surface database

freesurfer ¶

Contains functions for interfacing with freesurfer

`get_paths`(fs_subject, hemi[, type, …])	Retrive paths for all surfaces for a subject processed by freesurfer
`autorecon`(fs_subject[, type, parallel, n_cores])	Run Freesurfer’s autorecon-all command for a given freesurfer subject
`flatten`(fs_subject, hemi, patch[, …])	Perform flattening of a brain using freesurfer
`import_subj`(fs_subject[, cx_subject, …])	Imports a subject from freesurfer
`import_flat`(fs_subject, patch[, hemis, …])	Imports a flat brain from freesurfer
`show_surf`(subject, hemi, type[, patch, …])	Show a surface from a Freesurfer subject directory
`make_fiducial`(fs_subject[, …])	Make fiducial surface (halfway between white matter and pial surfaces)
`parse_surf`(filename)
`parse_curv`(filename)
`parse_patch`(filename)
`get_surf`(subject, hemi, type[, patch, …])	Read freesurfer surface file
`get_curv`(fs_subject, hemi[, type, …])	Load freesurfer curv file for a freesurfer subject
`write_dot`(fname, pts, polys[, name])
`read_dot`(fname, pts)
`write_decimated`(path, pts, polys)
`SpringLayout`(pts, polys[, dpts, pins, …])
`stretch_mwall`(pts, polys, mwall)

mapper ¶

`Mapper`(left, right, shape, subject, xfmname)	Maps data from epi volume onto surface using various projections
`get_mapper`(subject, xfmname[, type, recache])

mni ¶

Functions for finding MNI transforms for individual subjects and transforming functional data and surfaces to and from MNI space.

`compute_mni_transform`(subject, xfm[, template])	Compute transform from the space specified by xfm to MNI standard space.
`transform_to_mni`(volumedata, func_to_mni[, …])	Transform data in volumedata to MNI space, resample at the resolution of the atlas image.
`transform_surface_to_mni`(subject, surfname)	Transform the surface named surfname for subject called subject into MNI coordinates.
`transform_mni_to_subject`(subject, xfm, …)	Transform data in volarray from MNI space to functional space specified by xfm.

polyutils ¶

`Surface`(pts, polys)	Represents a single cortical hemisphere surface.
`Distortion`(flat, ref, polys)	Used to compute distortion metrics between fiducial and another (e.g.

segment ¶

Controls functions for segmentation of white/gray matter and other things in the brain.

`init_subject`(subject, filenames[, …])	Run the first initial segmentation for a subject’s anatomy (in Freesurfer).
`fix_wm`(subject)	Initializes an interface to make white matter edits to the surface.
`fix_pia`(subject)	Initializes an interface to make pial surface edits.
`cut_surface`(cx_subject, hemi[, name, …])	Initializes an interface to cut the segmented surface for flatmapping.

surfinfo ¶

Contains functions for computing various surface properties. Mostly wrappers for functions in polyutils.Surface and polyutils.Distortion.

`curvature`(outfile, subject[, smooth])	Compute smoothed mean curvature of the fiducial surface for the given subject and save it to outfile.
`distortion`(outfile, subject[, dist_type, smooth])	Compute distortion of flatmap relative to fiducial surface and save it at outfile.
`thickness`(outfile, subject)	Compute cortical thickness as the distance between corresponding pial and white matter vertices for the given subject.
`tissots_indicatrix`(outfile, sub[, radius, …])	Compute a Tissot’s indicatrix for the given subject and save the result to a file.
`flat_border`(outfile, subject)

utils ¶

Contain utility functions

`add_roi`(data[, name, open_inkscape, …])	Add new flatmap image to the ROI file for a subject.
`anat2epispace`(anatdata, subject, xfmname[, …])	Resamples data from anatomical space into epi space
`get_aseg_mask`(subject, aseg_name[, xfmname, …])	Return an epi space mask of the given ID from freesurfer’s automatic segmentation
`get_cmap`(name)	Gets a colormaps
`get_cortical_mask`(subject, xfmname[, type])	Gets the cortical mask for a particular transform
`get_ctmmap`(subject, **kwargs)	Parameters
`get_ctmpack`(subject[, types, method, level, …])	Creates ctm file for the specified input arguments.
`get_dropout`(subject, xfmname[, power])	Create a dropout Volume showing where EPI signal is very low.
`get_hemi_masks`(subject, xfmname[, type])	Returns a binary mask of the left and right hemisphere surface voxels for the given subject.
`get_roi_mask`(subject, xfmname[, roi, projection])	Return a mask for the given ROI(s)
`get_roi_masks`(subject, xfmname[, roi_list, …])	Return a dictionary of roi masks
`get_roi_verts`(subject[, roi, mask, overlay_file])	Return vertices for the given ROIs, or all ROIs if none are given.
`get_vox_dist`(subject, xfmname[, surface, …])	Get the distance (in mm) from each functional voxel to the closest point on the surface.
`make_movie`(stim, outfile[, fps, size])	Makes an .ogv movie
`vertex_to_voxel`(subject)	Parameters

volume ¶

Contains functions for working with volume data

`unmask`(mask, data)	Unmask the data, assuming it’s been masked.
`mosaic`(data[, dim, show])	Turns volume data into a mosaic, useful for quickly viewing volumetric data with radiological convention (left side of figure is right side of subject).
`epi2anatspace`(volumedata[, order])	Resample an epi volume data into anatomical space using scipy.
`anat2epispace`(anatdata, subject, xfmname[, …])	Resamples data from anatomical space into epi space
`epi2anatspace_fsl`(volumedata)	Resamples epi-space [data] into the anatomical space for the given [subject] using the given transformation [xfm].
`anat2epispace_fsl`(data, subject, xfmname)	Resamples anat-space data into the epi space for the given [subject] and transformation [xfm]
`show_slice`(dataview, **kwargs)
`show_mip`(data, **kwargs)	Display a maximum intensity projection for the data, using three subplots
`show_glass`(dataview[, pad])	Create a classic “glass brain” view of the data, with the outline

xfm ¶

Affine transformation class

Transform(xfm, reference)

A standard affine transform.