"""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)
else:
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)
fp.seek(0)
svg.rois.add_shape(name, binascii.b2a_base64(fp.read()).decode('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 sp.call(cmd)
[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:
https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/AnatomicalROI/FreeSurferColorLUT
... 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)
sp.call(shlex.split(fcmd))
[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', data=data_.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))
plt.cm.register_cmap(name,cmap)
else:
try:
cmap = plt.cm.get_cmap(name)
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 cortex.webgl.show()
"""
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
current filestore in use (`cortex.db.filestore`).
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='https://ndownloader.figshare.com/files/17827577?private_link=4871247dce31e188e758',
)
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()
wget.download(url, tmp_dir)
print('Downloaded subject {} to {}'.format(subject_id, tmp_dir))
# Un-tar to pycortex store
if pycortex_store is None:
# Default location is current filestore in cortex.db
pycortex_store = db.filestore
pycortex_store = os.path.expanduser(pycortex_store)
with tarfile.open(os.path.join(tmp_dir, subject_id + '.tar.gz'), "r:gz") as tar:
print("Extracting subject {} to {}".format(subject_id, pycortex_store))
tar.extractall(path=pycortex_store)
# reload all subjects from the filestore
db.reload_subjects()
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 = rotation_mat.dot(pts[:, :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)