Source code for cortex.utils

"""Contain utility functions
import io
import os
import h5py
import copy
import shutil
import binascii
import warnings
import numpy as np
import tarfile
import wget
import tempfile

from distutils.version import LooseVersion

from six import string_types
from importlib import import_module
from .database import db
from .volume import anat2epispace
from .options import config
from .freesurfer import fs_aseg_dict
from .polyutils import Surface
from . import formats
from .testing_utils import INKSCAPE_VERSION

class DocLoader(object):
    def __init__(self, func, mod, package):
        self._load = lambda: getattr(import_module(mod, package), func)

    def __call__(self, *args, **kwargs):
        return self._load()(*args, **kwargs)

    def __getattribute__(self, name):
        if name != "_load":
            return getattr(self._load(), name)
            return object.__getattribute__(self, name)

def get_roipack(*args, **kwargs):
    warnings.warn('Please use db.get_overlay instead', DeprecationWarning)
    return db.get_overlay(*args, **kwargs)

get_mapper = DocLoader("get_mapper", ".mapper", "cortex")

[docs]def get_ctmpack(subject, types=("inflated",), method="raw", level=0, recache=False, decimate=False, external_svg=None, overlays_available=None): """Creates ctm file for the specified input arguments. This is a cached file that specifies (1) the surfaces between which to interpolate (`types` argument), (2) the `method` to interpolate between surfaces Parameters ---------- subject : str Name of subject in pycortex stored types : tuple Surfaces between which to interpolate. method : str string specifying method of how inverse transforms for labels are computed (determines how labels are displayed on 3D viewer) one of ['mg2','raw'] recache : bool Whether to re-generate .ctm files. Can resolve some errors but takes more time to re-generate cached files. decimate : bool whether to decimate the mesh geometry of the hemispheres to reduce file size external_svg : str or None file string for .svg file containing alternative overlays for brain viewer. If None, the `overlays.svg` file for this subject (in the pycortex_store folder for the subejct) is used. overlays_available: tuple or None Which overlays in the svg file to include in the viewer. If None, all layers in the relevant svg file are included. Returns ------- ctmfile : """ lvlstr = ("%dd" if decimate else "%d")%level # Generates different cache files for each combination of disp_layers ctmcache = "%s_[{types}]_{method}_{level}_v3.json"%subject ctmcache = ctmcache.format(types=','.join(types), method=method, level=lvlstr) ctmfile = os.path.join(db.get_cache(subject), ctmcache) if os.path.exists(ctmfile) and not recache: return ctmfile print("Generating new ctm file...") from . import brainctm ptmap = brainctm.make_pack(ctmfile, subject, types=types, method=method, level=level, decimate=decimate, external_svg=external_svg, overlays_available=overlays_available) return ctmfile
[docs]def get_ctmmap(subject, **kwargs): """ Parameters ---------- subject : str Subject name Returns ------- lnew : rnew : """ from scipy.spatial import cKDTree from . import brainctm jsfile = get_ctmpack(subject, **kwargs) ctmfile = os.path.splitext(jsfile)[0]+".ctm" try: left, right = db.get_surf(subject, "pia") except IOError: left, right = db.get_surf(subject, "fiducial") lmap, rmap = cKDTree(left[0]), cKDTree(right[0]) left, right = brainctm.read_pack(ctmfile) lnew = lmap.query(left[0])[1] rnew = rmap.query(right[0])[1] return lnew, rnew
[docs]def get_cortical_mask(subject, xfmname, type='nearest'): """Gets the cortical mask for a particular transform Parameters ---------- subject : str Subject name xfmname : str Transform name type : str Mask type, one of {'cortical','thin','thick', 'nearest'}. 'cortical' is exactly the cortical ribbon, between the freesurfer-estimated white matter and pial surfaces; 'thin' is < 2mm away from fiducial surface; 'thick' is < 8mm away from fiducial surface. 'nearest' is nearest voxel only (??) Returns ------- mask : array boolean mask array for cortical voxels in functional space """ if type == 'cortical': ppts, polys = db.get_surf(subject, "pia", merge=True, nudge=False) wpts, polys = db.get_surf(subject, "wm", merge=True, nudge=False) thickness = np.sqrt(((ppts - wpts)**2).sum(1)) dist, idx = get_vox_dist(subject, xfmname) cortex = np.zeros(dist.shape, dtype=bool) verts = np.unique(idx) for i, vert in enumerate(verts): mask = idx == vert cortex[mask] = dist[mask] <= thickness[vert] if i % 100 == 0: print("%0.3f%%"%(i/float(len(verts)) * 100)) return cortex elif type in ('thick', 'thin'): dist, idx = get_vox_dist(subject, xfmname) return dist < dict(thick=8, thin=2)[type] else: return get_mapper(subject, xfmname, type=type).mask
[docs]def get_vox_dist(subject, xfmname, surface="fiducial", max_dist=np.inf): """Get the distance (in mm) from each functional voxel to the closest point on the surface. Parameters ---------- subject : str Name of the subject xfmname : str Name of the transform shape : tuple Output shape for the mask max_dist : nonnegative float, optional Limit computation to only voxels within `max_dist` mm of the surface. Makes computation orders of magnitude faster for high-resolution volumes. Returns ------- dist : ndarray Distance (in mm) to the closest point on the surface argdist : ndarray Point index for the closest point """ from scipy.spatial import cKDTree fiducial, polys = db.get_surf(subject, surface, merge=True) xfm = db.get_xfm(subject, xfmname) z, y, x = xfm.shape idx = np.mgrid[:x, :y, :z].reshape(3, -1).T mm = xfm.inv(idx) tree = cKDTree(fiducial) dist, argdist = tree.query(mm, distance_upper_bound=max_dist) dist.shape = (x,y,z) argdist.shape = (x,y,z) return dist.T, argdist.T
[docs]def get_hemi_masks(subject, xfmname, type='nearest'): '''Returns a binary mask of the left and right hemisphere surface voxels for the given subject. Parameters ---------- subject : str Name of subject xfmname : str Name of transform type : str Returns ------- ''' return get_mapper(subject, xfmname, type=type).hemimasks
[docs]def add_roi(data, name="new_roi", open_inkscape=True, add_path=True, overlay_file=None, **kwargs): """Add new flatmap image to the ROI file for a subject. (The subject is specified in creation of the data object) Creates a flatmap image from the `data` input, and adds that image as a sub-layer to the data layer in the rois.svg file stored for the subject in the pycortex database. Most often, this is data to be used for defining a region (or several regions) of interest, such as a localizer contrast (e.g. a t map of Faces > Houses). Use the **kwargs inputs to specify Parameters ---------- data : DataView The data used to generate the flatmap image. name : str, optional Name that will be assigned to the `data` sub-layer in the rois.svg file (e.g. 'Faces > Houses, t map, p<.005' or 'Retinotopy - Rotating Wedge') open_inkscape : bool, optional If True, Inkscape will automatically open the ROI file. add_path : bool, optional If True, also adds a sub-layer to the `rois` new SVG layer will automatically be created in the ROI group with the same `name` as the overlay. overlay_file : str, optional Custom overlays.svg file to use instead of the default one for this subject (if not None). Default None. kwargs : dict Passed to cortex.quickflat.make_png """ import subprocess as sp from . import quickflat from . import dataset dv = dataset.normalize(data) if isinstance(dv, dataset.Dataset): raise TypeError("Please specify a data view") svg = db.get_overlay(dv.subject, overlay_file=overlay_file) fp = io.BytesIO() quickflat.make_png(fp, dv, height=1024, with_rois=False, with_labels=False, **kwargs) svg.rois.add_shape(name, binascii.b2a_base64('utf-8'), add_path) if open_inkscape: inkscape_cmd = config.get('dependency_paths', 'inkscape') if LooseVersion(INKSCAPE_VERSION) < LooseVersion('1.0'): cmd = [inkscape_cmd, '-f', svg.svgfile] else: cmd = [inkscape_cmd, svg.svgfile] return
[docs]def get_roi_verts(subject, roi=None, mask=False, overlay_file=None): """Return vertices for the given ROIs, or all ROIs if none are given. Parameters ---------- subject : str Name of the subject roi : str, list or None, optional ROIs to fetch. Can be ROI name (string), a list of ROI names, or None, in which case all ROIs will be fetched. mask : bool if True, return a logical mask across vertices for the roi if False, return a list of indices for the ROI Returns ------- roidict : dict Dictionary of {roi name : roi verts}. ROI verts are for both hemispheres, with right hemisphere vertex numbers sequential after left hemisphere vertex numbers. """ # Get overlays svg = db.get_overlay(subject, overlay_file=overlay_file) # Get flat surface so we can figure out which verts are in medial wall # or in cuts # This assumes subject has flat surface, which they must to have ROIs. pts, polys = db.get_surf(subject, "flat", merge=True) goodpts = np.unique(polys) if roi is None: roi = svg.rois.shapes.keys() roidict = dict() if isinstance(roi, string_types): roi = [roi] for name in roi: roi_idx = np.intersect1d(svg.rois.get_mask(name), goodpts) if mask: roidict[name] = np.zeros(pts.shape[:1]) > 1 if np.any(roi_idx): roidict[name][roi_idx] = True else: warnings.warn("No vertices found in {}!".format(name)) else: roidict[name] = roi_idx return roidict
def get_roi_surf(subject, surf_type, roi): """Similar to get_roi_verts, but gets both the points and the polys for an roi. Parameters ---------- subject : str Name of subject surf_type : str Type of surface to return, probably in (fiducial, inflated, veryinflated, hyperinflated, superinflated, flat) roi : str Name of ROI to get the surface geometry for. Returns ------- pts, polys : (array, array) The points, specified in 3D space, as well as indices into pts specifying the polys. """ roi_verts_mask = get_roi_verts(subject, roi, mask=True) pts, polys = db.get_surf(subject, surf_type, merge=True, nudge=True) vert_idx = np.where(roi_verts_mask[roi])[0] vert_set = set(vert_idx) roi_polys = [] for i in xrange(np.shape(polys)[0]): if np.array(map(lambda x: x in vert_set, polys[i, :])).all(): roi_polys.append(polys[i, :]) reindexed_polys = [] vert_rev_hash_idx = {} for i, v in enumerate(vert_idx): vert_rev_hash_idx[v] = i for poly in roi_polys: reindexed_polys.append(map(vert_rev_hash_idx.get, poly)) return (pts[vert_idx], np.array(reindexed_polys))
[docs]def get_roi_mask(subject, xfmname, roi=None, projection='nearest'): """Return a mask for the given ROI(s) Deprecated - use get_roi_masks() Parameters ---------- subject : str Name of subject xfmname : str Name of transform roi : tuple Name of ROI(s) to get masks for. None gets all of them. projection : str Which mapper to use. Returns ------- output : dict Dict of ROIs and their masks """ warnings.warn('Deprecated! Use get_roi_masks') mapper = get_mapper(subject, xfmname, type=projection) rois = get_roi_verts(subject, roi=roi, mask=True) output = dict() for name, verts in list(rois.items()): # This is broken; unclear when/if backward mappers ever worked this way. #left, right = mapper.backwards(vert_mask) #output[name] = left + right output[name] = mapper.backwards(verts.astype(np.float)) # Threshold? return output
[docs]def get_aseg_mask(subject, aseg_name, xfmname=None, order=1, threshold=None, **kwargs): """Return an epi space mask of the given ID from freesurfer's automatic segmentation Parameters ---------- subject : str pycortex subject ID aseg_name : str or list Name of brain partition or partitions to return. See freesurfer web site for partition names: ... or inspect `cortex.freesurfer.fs_aseg_mask.keys()` Currently (2017.03) only the first 256 indices in the freesurfer lookup table are supported. If a name is provided that does not exactly match any of the freesurfer partitions, the function will search for all partitions that contain that name (caps are ignored). For example, 'white-matter' will generate a mask that combines masks for the following partitions: 'Right-Cerebral-White-Matter', 'Left-Cerebellum-White-Matter', 'Right-Cerebellum-White-Matter', and 'Left-Cerebral-White-Matter') xfmname : str Name for transform of mask to functional space. If `None`, anatomical-space mask is returned. order : int, [0-5] Order of spline interpolation for transform from anatomical to functional space (ignored if xfmname is None). 0 is like nearest neighbor; 1 returns bilinear interpolation of mask from anatomical space. To convert either of these volumes to a binary mask for voxel selection, set the `threshold` argument. Setting order > 1 is not recommended, as it will give values outside the range of 0-1. threshold : scalar Threshold value for aseg mask. If None, function returns result of spline interpolation of mask as transformed to functional space (will have continuous float values from 0-1) Returns ------- mask : array array with float or boolean values denoting the location of the requested cortical partition. Notes ----- See also get_anat(subject, type='aseg') """ from .freesurfer import fs_aseg_dict aseg = db.get_anat(subject, type="aseg").get_data().T if not isinstance(aseg_name, (list, tuple)): aseg_name = [aseg_name] mask = np.zeros(aseg.shape) for name in aseg_name: if name in fs_aseg_dict: tmp = aseg==fs_aseg_dict[name] else: # Combine all masks containing `name` (e.g. all masks with 'cerebellum' in the name) keys = [k for k in fs_aseg_dict.keys() if name.lower() in k.lower()] if len(keys) == 0: raise ValueError('Unknown aseg_name!') tmp = np.any(np.array([aseg==fs_aseg_dict[k] for k in keys]), axis=0) mask = np.logical_or(mask, tmp) if xfmname is not None: mask = anat2epispace(mask.astype(float), subject, xfmname, order=order, **kwargs) if threshold is not None: mask = mask > threshold return mask
[docs]def get_roi_masks(subject, xfmname, roi_list=None, gm_sampler='cortical', split_lr=False, allow_overlap=False, fail_for_missing_rois=True, exclude_empty_rois=False, threshold=None, return_dict=True, overlay_file=None): """Return a dictionary of roi masks This function returns a single 3D array with a separate numerical index for each ROI, Parameters ---------- subject : string pycortex subject ID xfmname : string pycortex transformation name roi_list : list or None List of names of ROIs to retrieve (e.g. ['FFA','OFA','EBA']). Names should match the ROI layers in the overlays.svg file for the `subject` specified. If None is provided (default), all available ROIs for the subject are returned. If 'Cortex' is included in roi_list*, a mask of all cortical voxels NOT included in other requested rois is included in the output. * works for gm_sampler = 'cortical', 'think', 'thick', or (any scalar value); does not work for mapper-based gray matter samplers. gm_sampler : scalar or string How to sample the cortical gray matter. Options are: <an integer> - Distance from fiducial surface to define ROI. Reasonable values for this input range from 1-3. The following will only work if you have used Freesurfer to define the subject's surface, and so have separate pial and white matter surfaces: 'cortical' - selection of all voxels with centers within the cortical ribbon (directly computed from distance of each voxel from fiducial surface) 'thick' - selection of voxels within 'thick' mask (see cortex.get_mask()) 'thin' - selection of voxels within 'thin' mask (see cortex.get_mask()) 'cortical-liberal' - selection of all voxels that have any part within the cortical ribbon ('line_nearest' mapper) 'cortical-conservative' - selection of only the closest voxel to each surface vertex ('nearest' mapper) mapper-based gm_samplers will return floating point values from 0-1 for each voxel (reflecting the fraction of that voxel inside the ROI) unless a threshold is provided. threshold : float [0-1] value used to convert probablistic ROI values to a boolean mask for the ROI. split_lr : bool Whether to separate ROIs in to left and right hemispheres (e.g., 'V1' becomes 'V1_L' and 'V1_R') allow_overlap : bool Whether to allow ROIs to include voxels in other ROIs (default:False). This should only be relevant if (a) spline shapes defining ROIs in overlays.svg overlap at all, or (b) a low threshold is set for a mapper-based gm_sampler fail_for_missing_rois : bool Whether to fail if one or more of the rois specified in roi_list are not defined in the overlays.svg file exclude_empty_rois : bool Whether to fail if an ROI that is present in the overlays.svg file contains no voxels due to the scan not targeting that region of the brain. return_dict : bool If True (default), function returns a dictionary of ROI masks; if False, a volume with integer indices for each ROI (similar to Freesurfer's aseg masks) and a dictionary of how the indices map to ROI names are returned. Returns ------- roi_masks : dict Dictionary of arrays; keys are ROI names, values are roi masks. - OR - index_volume, index_labels : array, dict `index_volume` is a 3D array with a separate numerical index value for each ROI. Index values in the left hemisphere are negative. (For example, if V1 in the right hemisphere is 1, then V1 in the left hemisphere will be -1). `index_labels` is a dict that maps roi names to index values (e.g. {'V1': 1}). Notes ----- Some gm_samplers may fail if you have very high-resolution data (i.e., with voxels on the order of the spacing between vertices in your cortical mesh). In such cases, there may be voxels in the middle of your ROI that are not assigned to the ROI (because no vertex falls within that voxel). For such cases, it is recommended to use 'cortical', 'thick', or 'thin' as your `gm_sampler`. """ # Convert mapper names to pycortex sampler types mapper_dict = {'cortical-conservative':'nearest', 'cortical-liberal':'line_nearest'} # Method use_mapper = gm_sampler in mapper_dict use_cortex_mask = (gm_sampler in ('cortical', 'thick', 'thin')) or not isinstance(gm_sampler, string_types) if not (use_mapper or use_cortex_mask): raise ValueError('Unknown gray matter sampler (gm_sampler)!') # Initialize roi_voxels = {} pct_coverage = {} # Catch single-ROI input if isinstance(roi_list, string_types): roi_list = [roi_list] if not return_dict: split_lr = True if use_mapper and threshold is None: raise Exception("You must set a threshold for gm_mapper='%s' if you want an indexed volume output"%gm_mapper) # Start with vertices if roi_list is None: roi_verts = get_roi_verts(subject, mask=use_mapper, overlay_file=overlay_file) roi_list = list(roi_verts.keys()) else: tmp_list = [r for r in roi_list if not r=='Cortex'] try: roi_verts = get_roi_verts(subject, roi=tmp_list, mask=use_mapper, overlay_file=overlay_file) except KeyError as key: if fail_for_missing_rois: raise KeyError("Requested ROI {} not found in overlays.svg!".format(key)) else: roi_verts = get_roi_verts(subject, roi=None, mask=use_mapper, overlay_file=overlay_file) missing = [r for r in roi_list if not r in roi_verts.keys()+['Cortex']] roi_verts = dict((roi, verts) for roi, verts in roi_verts.items() if roi in roi_list) roi_list = list(set(roi_list)-set(missing)) print('Requested ROI(s) {} not found in overlays.svg!'.format(missing)) # Get (a) indices for nearest vertex to each voxel # and (b) distance from each voxel to nearest vertex in fiducial surface if (use_cortex_mask or split_lr) or (not return_dict): vox_dst, vox_idx = get_vox_dist(subject, xfmname) if use_mapper: mapper = get_mapper(subject, xfmname, type=mapper_dict[gm_sampler]) elif use_cortex_mask: if isinstance(gm_sampler, string_types): cortex_mask = db.get_mask(subject, xfmname, type=gm_sampler) else: cortex_mask = vox_dst <= gm_sampler # Loop over ROIs to map vertices to volume, using mapper or cortex mask + vertex indices for roi in roi_list: if roi not in roi_verts: if not roi=='Cortex': print("ROI {} not found...".format(roi)) continue if use_mapper: roi_voxels[roi] = mapper.backwards(roi_verts[roi].astype(np.float)) # Optionally threshold probablistic values returned by mapper if threshold is not None: roi_voxels[roi] = roi_voxels[roi] > threshold # Check for partial / empty rois: vert_in_scan = np.hstack([np.array((m>0).sum(1)).flatten() for m in mapper.masks]) vert_in_scan = vert_in_scan[roi_verts[roi]] elif use_cortex_mask: vox_in_roi = np.in1d(vox_idx.flatten(), roi_verts[roi]).reshape(vox_idx.shape) roi_voxels[roi] = vox_in_roi & cortex_mask # This is not accurate... because vox_idx only contains the indices of the *nearest* # vertex to each voxel, it excludes many vertices. I can't think of a way to compute # this accurately for non-mapper gm_samplers for now... ML 2017.07.14 vert_in_scan = np.in1d(roi_verts[roi], vox_idx[cortex_mask]) # Compute ROI coverage pct_coverage[roi] = vert_in_scan.mean() * 100 if use_mapper: print("Found %0.2f%% of %s"%(pct_coverage[roi], roi)) # Create cortex mask all_mask = np.array(list(roi_voxels.values())).sum(0) if 'Cortex' in roi_list: if use_mapper: # cortex_mask isn't defined / exactly definable if you're using a mapper print("Cortex roi not included b/c currently not compatible with your selection for gm_sampler") _ = roi_list.pop(roi_list.index('Cortex')) else: roi_voxels['Cortex'] = (all_mask==0) & cortex_mask # Optionally cull voxels assigned to > 1 ROI due to partly overlapping ROI splines # in inkscape overlays.svg file: if not allow_overlap: print('Cutting {} overlapping voxels (should be < ~50)'.format(np.sum(all_mask > 1))) for roi in roi_list: roi_voxels[roi][all_mask > 1] = False # Split left / right hemispheres if desired # There ought to be a more succinct way to do this - get_hemi_masks only does the cortical # ribbon, and is not guaranteed to have all voxels in the cortex_mask specified in this fn if split_lr: left_verts, right_verts = db.get_surf(subject, "flat", merge=False, nudge=True) left_mask = vox_idx < len(np.unique(left_verts[1])) right_mask = np.logical_not(left_mask) roi_voxels_lr = {} for roi in roi_list: roi_voxels_lr[roi+'_L'] = copy.copy(roi_voxels[roi]) # & left_mask roi_voxels_lr[roi+'_L'][right_mask] = False # ? roi_voxels_lr[roi+'_R'] = copy.copy(roi_voxels[roi]) # & right_mask roi_voxels_lr[roi+'_R'][left_mask] = False # ? output = roi_voxels_lr else: output = roi_voxels # Check percent coverage / optionally cull emtpy ROIs for roi in set(roi_list)-set(['Cortex']): if pct_coverage[roi] < 100: # if not np.any(mask) : reject ROI if pct_coverage[roi]==0: warnings.warn('ROI %s is entirely missing from your scan protocol!'%(roi)) if exclude_empty_rois: if split_lr: _ = output.pop(roi+'_L') _ = output.pop(roi+'_R') else: _ = output.pop(roi) else: # I think this is the only one for which this works correctly... if gm_sampler=='cortical-conservative': warnings.warn('ROI %s is only %0.2f%% contained in your scan protocol!'%(roi, pct_coverage[roi])) # Support alternative outputs for backward compatibility if return_dict: return output else: idx_vol = np.zeros(vox_idx.shape, dtype=np.int64) idx_labels = {} for iroi, roi in enumerate(roi_list, 1): idx_vol[roi_voxels[roi]] = iroi idx_labels[roi] = iroi idx_vol[left_mask] *= -1 return idx_vol, idx_labels
[docs]def get_dropout(subject, xfmname, power=20): """Create a dropout Volume showing where EPI signal is very low. Parameters ---------- subject : str Name of subject xfmname : str Name of transform power : Returns ------- volume : dataview Pycortex volume of low signal locations """ xfm = db.get_xfm(subject, xfmname) rawdata = xfm.reference.get_data().T.astype(np.float32) # Collapse epi across time if it's 4D if rawdata.ndim > 3: rawdata = rawdata.mean(0) rawdata[rawdata==0] = np.mean(rawdata[rawdata!=0]) normdata = (rawdata - rawdata.min()) / (rawdata.max() - rawdata.min()) normdata = (1 - normdata) ** power from .dataset import Volume return Volume(normdata, subject, xfmname)
[docs]def make_movie(stim, outfile, fps=15, size="640x480"): """Makes an .ogv movie A simple wrapper for ffmpeg. Calls: "ffmpeg -r {fps} -i {infile} -b 4800k -g 30 -s {size} -vcodec libtheora {outfile}.ogv" Parameters ---------- stim : outfile : str fps : float refresh rate of the stimulus size : str resolution of the movie out Returns ------- """ import shlex import subprocess as sp cmd = "ffmpeg -r {fps} -i {infile} -b 4800k -g 30 -s {size} -vcodec libtheora {outfile}.ogv" fcmd = cmd.format(infile=stim, size=size, fps=fps, outfile=outfile)
[docs]def vertex_to_voxel(subject): # Am I deprecated in favor of mappers??? Maybe? """ Parameters ---------- subject : str Name of subject Returns ------- """ max_thickness = db.get_surfinfo(subject, "thickness").data.max() # Get distance from each voxel to each vertex on each surface fid_dist, fid_verts = get_vox_dist(subject, "identity", "fiducial", max_thickness) wm_dist, wm_verts = get_vox_dist(subject, "identity", "wm", max_thickness) pia_dist, pia_verts = get_vox_dist(subject, "identity", "pia", max_thickness) # Get nearest vertex on any surface for each voxel all_dist, all_verts = fid_dist, fid_verts wm_closer = wm_dist < all_dist all_dist[wm_closer] = wm_dist[wm_closer] all_verts[wm_closer] = wm_verts[wm_closer] pia_closer = pia_dist < all_dist all_dist[pia_closer] = pia_dist[pia_closer] all_verts[pia_closer] = pia_verts[pia_closer] return all_verts
def _set_edge_distance_graph_attribute(graph, pts, polys): ''' adds the attribute 'edge distance' to a graph ''' import networkx as nx l2_distance = lambda v1, v2: np.linalg.norm(pts[v1] - pts[v2]) heuristic = l2_distance # A* heuristic if not nx.get_edge_attributes(graph, 'distance'): # Add edge distances as an attribute to this graph if it isn't there edge_distances = dict() for x,y,z in polys: edge_distances[(x,y)] = heuristic(x,y) edge_distances[(y,x)] = heuristic(y,x) edge_distances[(y,z)] = heuristic(y,z) edge_distances[(z,y)] = heuristic(z,y) edge_distances[(x,z)] = heuristic(x,z) edge_distances[(z,x)] = heuristic(z,x) nx.set_edge_attributes(graph, edge_distances, name='distance') def get_shared_voxels(subject, xfmname, hemi="both", merge=True, use_astar=True): '''Return voxels that are shared by multiple vertices, and for each such voxel, also returns the mutually farthest pair of vertices mapping to the voxel Parameters ---------- subject : str Name of the subject xfmname : str Name of the transform hemi : str, optional Which hemisphere to return. For now, only 'lh' or 'rh' merge : bool, optinal Join the hemispheres, if requesting both use_astar: bool, optional Toggle to decide whether to use A* seach or geodesic paths for the shortest paths Returns ------- vox_vert_array: np.array, array of dimensions # voxels X 3, columns being: (vox_idx, farthest_pair[0], farthest_pair[1]) ''' from scipy.sparse import find as sparse_find import networkx as nx Lmask, Rmask = get_mapper(subject, xfmname).masks # Get masks for left and right hemisphere if hemi == 'both': hemispheres = ['lh', 'rh'] else: hemispheres = [hemi] out = [] for hem in hemispheres: if hem == 'lh': mask = Lmask else: mask = Rmask all_voxels = mask.tolil().transpose().rows # Map from voxels to verts vert_to_vox_map = dict(zip(*(sparse_find(mask)[:2]))) # From verts to vox pts_fid, polys_fid = db.get_surf(subject, 'fiducial', hem) # Get the fiducial surface surf = Surface(pts_fid, polys_fid) #Get the fiducial surface graph = surf.graph _set_edge_distance_graph_attribute(graph, pts_fid, polys_fid) l2_distance = lambda v1, v2: np.linalg.norm(pts_fid[v1] - pts_fid[v2]) heuristic = l2_distance # A* heuristic if use_astar: shortest_path = lambda a, b: nx.astar_path(graph, a, b, heuristic=heuristic, weight='distance') # Find approximate shortest paths using A* search else: shortest_path = surf.geodesic_path # Find shortest paths using geodesic distances vox_vert_list = [] for vox_idx, vox in enumerate(all_voxels): if len(vox) > 1: # If the voxel maps to multiple vertices vox = np.array(vox).astype(int) for v1 in range(vox.size-1): vert1 = vox[v1] if vert1 in vert_to_vox_map: # If the vertex is a valid vertex for v2 in range(v1+1, vox.size): vert2 = vox[v2] if vert2 in vert_to_vox_map: # If the vertex is a valid vertex path = shortest_path(vert1, vert2) # Test whether any vertex in path goes out of the voxel stays_in_voxel = all([(v in vert_to_vox_map) and (vert_to_vox_map[v] == vox_idx) for v in path]) if not stays_in_voxel: vox_vert_list.append([vox_idx, vert1, vert2]) tmp = np.array(vox_vert_list) # Add offset for right hem voxels if hem=='rh': tmp[:, 1:3] += Lmask.shape[0] out.append(tmp) if hemi in ('lh', 'rh'): return out[0] else: if merge: return np.vstack(out) else: return tuple(out) def load_sparse_array(fname, varname): """Load a numpy sparse array from an hdf file Parameters ---------- fname: string file name containing array to be loaded varname: string name of variable to be loaded Notes ----- This function relies on variables being stored with specific naming conventions, so cannot be used to load arbitrary sparse arrays. """ import scipy.sparse with h5py.File(fname) as hf: data = (hf['%s_data'%varname], hf['%s_indices'%varname], hf['%s_indptr'%varname]) sparsemat = scipy.sparse.csr_matrix(data, shape=hf['%s_shape'%varname]) return sparsemat def save_sparse_array(fname, data, varname, mode='a'): """Save a numpy sparse array to an hdf file Results in relatively smaller file size than numpy.savez Parameters ---------- fname : string file name to save data : sparse array data to save varname : string name of variable to save mode : string write / append mode set, one of ['w','a'] (passed to h5py.File()) """ import scipy.sparse if not isinstance(data, scipy.sparse.csr.csr_matrix): data_ = scipy.sparse.csr_matrix(data) else: data_ = data with h5py.File(fname, mode=mode) as hf: # Save indices hf.create_dataset(varname + '_indices', data=data_.indices, compression='gzip') # Save data hf.create_dataset(varname + '_data',, compression='gzip') # Save indptr hf.create_dataset(varname + '_indptr', data=data_.indptr, compression='gzip') # Save shape hf.create_dataset(varname + '_shape', data=data_.shape, compression='gzip')
[docs]def get_cmap(name): """Gets a colormaps Parameters ---------- name : str Name of colormap to get Returns ------- cmap : ListedColormap Matplotlib colormap object """ import matplotlib.pyplot as plt from matplotlib import colors # unknown colormap, test whether it's in pycortex colormaps cmapdir = config.get('webgl', 'colormaps') colormaps = os.listdir(cmapdir) colormaps = sorted([c for c in colormaps if '.png' in c]) colormaps = dict((c[:-4], os.path.join(cmapdir, c)) for c in colormaps) if name in colormaps: I = plt.imread(colormaps[name]) cmap = colors.ListedColormap(np.squeeze(I)),cmap) else: try: cmap = except: raise Exception('Unkown color map!') return cmap
def add_cmap(cmap, name, cmapdir=None): """Add a colormap to pycortex This stores a matplotlib colormap in the pycortex filestore, such that it can be used in the webgl viewer in pycortex. See [] for more information about how to generate colormaps in matplotlib Parameters ---------- cmap : matplotlib colormap Color map to be saved name : Name for colormap, e.g. 'jet', 'blue_to_yellow', etc. This will be a file name, so no weird characters. This name will also be used to specify this colormap in future calls to cortex.quickflat.make_figure() or """ import matplotlib.pyplot as plt from matplotlib import colors x = np.linspace(0, 1, 256) cmap_im = cmap(x).reshape((1,256,4)) if cmapdir is None: # Probably won't work due to permissions... cmapdir = config.get('webgl', 'colormaps') plt.imsave(os.path.join(cmapdir, name), cmap_im) def download_subject(subject_id='fsaverage', url=None, pycortex_store=None, download_again=False): """Download subjects to pycortex store Parameters ---------- subject_id : string subject identifying string in pycortex. This assumes that the file downloaded from some URL is of the form <subject_id>.tar.gz url: string or None URL from which to download. Not necessary to specify for subjects known to pycortex (None is OK). Known subjects will have a default URL. Currently,the only known subjects is 'fsaverage', but there are plans to add more in the future. If provided, URL overrides `subject_id` pycortex_store : string or None Directory to which to put the new subject folder. If None, defaults to the `filestore` variable specified in the pycortex config file. download_again : bool Download the data again even if the subject id is already present in the pycortex's database. """ if subject_id in db.subjects and not download_again: warnings.warn( "{} is already present in the database. " "Set download_again to True if you wish to download " "the subject again.".format(subject_id)) return # Map codes to URLs; more coming eventually id_to_url = dict(fsaverage='', ) if url is None: if not subject_id in id_to_url: raise ValueError('Unknown subject_id!') url = id_to_url[subject_id] print("Downloading from: {}".format(url)) # Download to temp dir tmp_dir = tempfile.gettempdir(), tmp_dir) print('Downloaded subject {} to {}'.format(subject_id, tmp_dir)) # Un-tar to pycortex store if pycortex_store is None: # Default location is config file pycortex store. pycortex_store = config.get('basic', 'filestore') pycortex_store = os.path.expanduser(pycortex_store) with, subject_id + '.tar.gz'), "r:gz") as tar: print("Extracting subject {} to {}".format(subject_id, pycortex_store)) tar.extractall(path=pycortex_store) def rotate_flatmap(surf_id, theta, plot=False): """Rotate flatmap to be less V-shaped Parameters ---------- surf_id : str pycortex surface identifier theta : scalar angle in degrees to rotate flatmaps (rotation is clockwise for right hemisphere and counter-clockwise for left) plot : bool Whether to make a coarse plot to visualize the changes """ # Lazy load of matplotlib import matplotlib.pyplot as plt paths = db.get_paths(surf_id)['surfs']['flat'] theta = np.radians(theta) if plot: fig, axs = plt.subplots(2, 2) for j, hem in enumerate(('lh','rh')): this_file = paths[hem] pts, polys = formats.read_gii(this_file) # Rotate clockwise (- rotation) for RH, counter-clockwise (+ rotation) for LH if hem == 'rh': rtheta = - theta else: rtheta = copy.copy(theta) rotation_mat = np.array([[np.cos(rtheta), -np.sin(rtheta)], [np.sin(rtheta), np.cos(rtheta)]]) rotated =[:, :2].T).T pts_new = pts.copy() pts_new[:, :2] = rotated new_file, bkup_num = copy.copy(this_file), 0 while os.path.exists(new_file): new_file = this_file.replace('.gii', '_rotbkup%02d.gii'%bkup_num) bkup_num += 1 print('Backing up file at %s...' % new_file) shutil.copy(this_file, new_file) formats.write_gii(this_file, pts_new, polys) print('Overwriting %s...' % this_file) if plot: axs[0,j].plot(*pts[::100, :2].T, marker='r.') axs[0,j].axis('equal') axs[1,j].plot(*pts_new[::100, :2].T, marker='b.') axs[1,j].axis('equal') # Remove and back up overlays file overlay_file = db.get_paths(surf_id)['overlays'] shutil.copy(overlay_file, overlay_file.replace('.svg', '_rotbkup%02d.svg'%bkup_num)) os.unlink(overlay_file) # Regenerate file svg = db.get_overlay(surf_id)