def reset_dataviews(self):
#in principle it might be useful to do some more cleanup here
self.display_mode = 'normal'
self.dv_3d = DVMayavi(self)
self.dv_mat = DVMatrix(self)
self.dv_circ = DVCircle(self)
self.chg_scalar_colorbar()
def __init__(self,
name,
lab_pos,
labnam,
srf,
labv,
gui=None,
adj=None,
soft_max_edges=20000,
**kwargs):
super(Dataset, self).__init__(**kwargs)
self.gui = gui
self.name = name
self.opts = DisplayOptions(self)
self.scalar_display_settings = ScalarDisplaySettings(self)
#this is effectively load_parc
self.lab_pos = lab_pos
self.labnam = labnam
self.srf = srf
self.labv = labv
self.nr_labels = len(labnam)
#load_parc redundantly sets the current display but oh well.
#self.load_parc(lab_pos,labnam,srf,labv,
# init_display.subject_name,init_display.parc_name)
#if adj is None, it means it will be guaranteed to be supplied later
#by the user
#this is load adj, except without initializing nonexistent dataviews
if adj is not None:
self.adj = adj
self.soft_max_edges = soft_max_edges
self.pos_helper_gen()
#flip adj ord should already be done to the preprocessed adj
self.adj_helper_gen()
self.color_legend = ColorLegend()
self.node_colors_gen()
self.dv_3d = DVMayavi(self)
self.dv_mat = DVMatrix(self)
self.dv_circ = DVCircle(self)
self.chg_scalar_colorbar()
def reset_dataviews(self): #in principle it might be useful to do some more cleanup here self.display_mode='normal' self.dv_3d=DVMayavi(self) self.dv_mat=DVMatrix(self) self.dv_circ=DVCircle(self) self.chg_scalar_colorbar()
def __init__(self,name,lab_pos,labnam,srf,labv, gui=None,adj=None,soft_max_edges=20000,**kwargs): super(Dataset,self).__init__(**kwargs) self.gui=gui self.name=name self.opts=DisplayOptions(self) self.scalar_display_settings=ScalarDisplaySettings(self) #this is effectively load_parc self.lab_pos=lab_pos self.labnam=labnam self.srf=srf self.labv=labv self.nr_labels=len(labnam) #load_parc redundantly sets the current display but oh well. #self.load_parc(lab_pos,labnam,srf,labv, # init_display.subject_name,init_display.parc_name) #if adj is None, it means it will be guaranteed to be supplied later #by the user #this is load adj, except without initializing nonexistent dataviews if adj is not None: self.adj=adj self.soft_max_edges=soft_max_edges self.pos_helper_gen() #flip adj ord should already be done to the preprocessed adj self.adj_helper_gen() self.color_legend=ColorLegend() self.node_colors_gen() self.dv_3d=DVMayavi(self) self.dv_mat=DVMatrix(self) self.dv_circ=DVCircle(self) self.chg_scalar_colorbar()
class Dataset(HasTraits):
########################################################################
# FUNDAMENTALLY NECESSARY DATA
########################################################################
name=Str #give this dataset a name
gui=Any #symbolic reference to a modular cvu
nr_labels=Int
nr_edges=Int
labnam=List(Str)
#adjlabfile=File
#the adjlabfile is not needed. this is only kept on hand to pass it
#around if specified as CLI arg. it is only used upon loading an adjmat.
#so just have adjmat loading be a part of dataset creation and get rid of
#keeping track of this
adj=Any #NxN np.ndarray
adj_thresdiag=Property(depends_on='adj') #NxN np.ndarray
@cached_property
def _get_adj_thresdiag(self):
adjt = self.adj.copy()
adjt[np.where(np.eye(self.nr_labels))]=np.min(adjt[np.where(adjt)])
return adjt
starts=Any #Ex3 np.ndarray
vecs=Any #Ex3 np.ndarray
edges=Any #Ex2 np.ndarray(int)
srf=Instance(SurfData)
#labv=List(Instance(mne.Label))
#all that is needed from this is a map of name->vertex
#this is a considerable portion of the data contained in a label but still
#only perhaps 15%. To make lightweight, extract this from labv
#TODO convert it to that
labv=Dict #is an OrderedDict in parcellation order
lab_pos=Any #Nx3 np.ndarray
#########################################################################
# CRITICAL NONADJUSTABLE DATA WITHOUT WHICH DISPLAY CANNOT EXIST
#########################################################################
dv_3d = Either(Instance(DataView),None)
dv_mat = Either(Instance(DataView),None)
dv_circ = Either(Instance(DataView),None)
soft_max_edges=Int
adjdat=Any #Ex1 np.ndarray
left=Any #Nx1 np.ndarray(bool)
right=Any #Nx1 np.ndarray(bool)
interhemi=Any #Nx1 np.ndarray(bool)
masked=Any #Nx1 np.ndarray(bool)
lhnodes=Property(depends_on='labnam') #Nx1 np.ndarray(int)
rhnodes=Property(depends_on='labnam') #Nx1 np.ndarray(int)
@cached_property
def _get_lhnodes(self):
return np.where(map(lambda r:r[0]=='l',self.labnam))[0]
@cached_property
def _get_rhnodes(self):
return np.where(map(lambda r:r[0]=='r',self.labnam))[0]
#TODO
node_colors = Any #Nx3 np.ndarray
#node_colors represents the colors held by the nodes. the current value of
#node_colors depends on the current policy (i.e. the current display mode).
#however, don't take this all too literally. depending on the current
#policy, the dataviews may choose to ignore what is in node_colors and
#use some different color.
#this is always true of Mayavi views, who can't use the node colors at all.
#because mayavi doesn't play nice with true colors (this could be fixed
#if mayavi is fixed). it is also true of the other plots in scalar mode,
#but when scalars are not specified for those dataviews.
#node_colors_default will be set uniquely for each parcellation and can
#thus be different for different datasets.
#group_colors is more subtle; it can in principle be set uniquely for each
#parcellation as long as the parcellations don't conform to aparc. for
#instance, destrieux parc has different group colors. right now i'm a long
#way away from dealing with this but i think in a month it will be prudent
#to just have the dataset capture both of these variables
node_colors_default=List
node_labels_numberless=List(Str)
group_colors=List
nr_groups=Int
group_labels=List(Str)
color_legend=Instance(ColorLegend)
module_colors=List
default_glass_brain_color=Constant((.82,.82,.82))
#########################################################################
# ASSOCIATED STATISTICAL AND ANALYTICAL DATA
#########################################################################
node_scalars=Dict
scalar_display_settings=Instance(ScalarDisplaySettings)
#TODO make modules a dictionary
modules=List
nr_modules=Int
graph_stats=Dict
#########################################################################
# ASSOCIATED DISPLAY OPTIONS AND DISPLAY STATE (ADJUSTABLE/TRANSIENT)
#########################################################################
opts=Instance(DisplayOptions)
display_mode=Enum('normal','scalar','module_single','module_multi')
reset_thresh=Property(Method)
def _get_reset_thresh(self):
if self.opts.thresh_type=='prop': return self.prop_thresh
elif self.opts.thresh_type=='abs': return self.abs_thresh
thresval=Float
curr_node=Either(Int,None)
cur_module=Either(Int,'custom',None)
custom_module=List
########################################################################
# SETUP
########################################################################
def __init__(self,name,lab_pos,labnam,srf,labv,
gui=None,adj=None,soft_max_edges=20000,**kwargs):
super(Dataset,self).__init__(**kwargs)
self.gui=gui
self.name=name
self.opts=DisplayOptions(self)
self.scalar_display_settings=ScalarDisplaySettings(self)
#this is effectively load_parc
self.lab_pos=lab_pos
self.labnam=labnam
self.srf=srf
self.labv=labv
self.nr_labels=len(labnam)
#load_parc redundantly sets the current display but oh well.
#self.load_parc(lab_pos,labnam,srf,labv,
# init_display.subject_name,init_display.parc_name)
#if adj is None, it means it will be guaranteed to be supplied later
#by the user
#this is load adj, except without initializing nonexistent dataviews
if adj is not None:
self.adj=adj
self.soft_max_edges=soft_max_edges
self.pos_helper_gen()
#flip adj ord should already be done to the preprocessed adj
self.adj_helper_gen()
self.color_legend=ColorLegend()
self.node_colors_gen()
self.dv_3d=DVMayavi(self)
self.dv_mat=DVMatrix(self)
self.dv_circ=DVCircle(self)
self.chg_scalar_colorbar()
def __str__(self): return self.name
def __repr__(self): return self.name
def __getitem__(self,key):
if key==0: return self
elif key==1: return self.name
else: raise KeyError('Invalid indexing to dataset. Dataset indexing '
'is implemented to appease CheckListEditor and can only be 0 or 1.')
########################################################################
# GEN METHODS
########################################################################
#preconditions: lab_pos has been set.
def pos_helper_gen(self,reset_scalars=True):
self.nr_labels = n = len(self.lab_pos)
self.nr_edges = self.nr_labels*(self.nr_labels-1)//2
#self.starts = np.zeros((self.nr_edges,3),dtype=float)
#self.vecs = np.zeros((self.nr_edges,3),dtype=float)
#self.edges = np.zeros((self.nr_edges,2),dtype=int)
#i=0
#for r2 in xrange(0,self.nr_labels,1):
# for r1 in xrange(0,r2,1):
#self.starts[i,:] = self.lab_pos[r1]
#self.vecs[i,:] = self.lab_pos[r2]-self.lab_pos[r1]
#self.edges[i,0],self.edges[i,1] = r1,r2
#i+=1
tri_ixes = np.triu(np.ones((n,n)),1)
ixes, = np.where(tri_ixes.flat)
A_r = np.tile(self.lab_pos,(n,1,1))
self.starts = np.reshape(A_r,(n*n,3))[ixes,:]
self.vecs = np.reshape(A_r-np.transpose(A_r,(1,0,2)),(n*n,3))[ixes,:]
self.edges = np.transpose(np.where(tri_ixes.T))[:,::-1]
#pos_helper_gen is now only called from load adj. The reason it is
#because it can change on all adj changes because of the soft
#cap. The number of edges can differ between adjmats because of the
#soft cap and all of the positions need to be recalculated if it does.
#pos_helper_gen really only has to do with edge positions. Node and
#surf positions dont depend on it at all.
#TODO possibly, keep track of the soft cap and do nothing if it hasn't
#changed
#RESPONSE: yes but this check should be done in adj_load
if reset_scalars:
self.node_scalars = {}
self.display_mode='normal'
#precondition: adj_helper_gen() must be run after pos_helper_gen()
def adj_helper_gen(self):
self.nr_edges = self.nr_labels*(self.nr_labels-1)//2
self.adjdat = np.zeros((self.nr_edges),dtype=float)
self.interhemi = np.zeros((self.nr_edges),dtype=bool)
self.left = np.zeros((self.nr_edges),dtype=bool)
self.right = np.zeros((self.nr_edges),dtype=bool)
self.masked = np.zeros((self.nr_edges),dtype=bool)
i=0
self.adj[xrange(self.nr_labels),xrange(self.nr_labels)]=0
#for r2 in xrange(0,self.nr_labels,1):
#self.adj[r2][r2]=0
#for r1 in xrange(0,r2,1):
#self.adjdat[i] = self.adj[r1][r2]
#self.interhemi[i] = self.labnam[r1][0] != self.labnam[r2][0]
#self.left[i] = self.labnam[r1][0]==self.labnam[r2][0]=='l'
#self.right[i] = self.labnam[r1][0]==self.labnam[r2][0]=='r'
#i+=1
n = self.nr_labels
ixes, = np.where(np.triu(np.ones((n,n)),1).flat)
self.adjdat = self.adj.flat[::-1][ixes][::-1]
from parsing_utils import same_hemi
sh=np.vectorize(same_hemi)
L_r = np.tile(self.labnam,(self.nr_labels,1))
self.interhemi = np.logical_not(sh(L_r,L_r.T)).flat[::-1][ixes][::-1]
self.left = sh(L_r,L_r.T,'l').flat[::-1][ixes][::-1]
self.right = sh(L_r,L_r.T,'r').flat[::-1][ixes][::-1]
#remove all but the soft_max_edges largest connections
if self.nr_edges > self.soft_max_edges:
cutoff = sorted(self.adjdat)[self.nr_edges-self.soft_max_edges-1]
zi = np.where(self.adjdat>=cutoff)
# if way way too many edges remain, make it a hard max
# this happens in DTI data which is very sparse, the cutoff is 0
if len(zi[0])>(self.soft_max_edges+200):
zi=np.where(self.adjdat>cutoff)
self.starts=self.starts[zi[0],:]
self.vecs=self.vecs[zi[0],:]
self.edges=self.edges[zi[0],:]
self.adjdat=self.adjdat[zi[0]]
self.interhemi=self.interhemi[zi[0]]
self.left=self.left[zi[0]]
self.right=self.right[zi[0]]
self.nr_edges=len(self.adjdat)
self.verbose_msg(str(self.nr_edges)+" total connections")
#sort the adjdat
sort_idx=np.argsort(self.adjdat,axis=0)
self.adjdat=self.adjdat[sort_idx].squeeze()
self.edges=self.edges[sort_idx].squeeze()
self.starts=self.starts[sort_idx].squeeze()
self.vecs=self.vecs[sort_idx].squeeze()
self.left=self.left[sort_idx].squeeze()
self.right=self.right[sort_idx].squeeze()
self.interhemi=self.interhemi[sort_idx].squeeze()
self.masked=self.masked[sort_idx].squeeze() #just to prune
#try to auto-set the threshold to a reasonable value
if self.nr_edges < 500:
self.opts.pthresh=.01
else:
thr = (self.nr_edges - 500) / (self.nr_edges)
self.opts.pthresh=thr
self.opts.thresh_type = 'prop'
self.display_mode='normal'
def node_colors_gen(self):
#node groups could change upon loading a new parcellation
hi_contrast_clist= ('#26ed1a','#eaf60b','#e726f4','#002aff','#05d5d5',
'#f4a5e0','#bbb27e','#641179','#068c40')
hi_contrast_cmap=LinearSegmentedColormap.from_list('hi_contrast',
hi_contrast_clist)
#labels are assumed to start with lh_ and rh_
self.node_labels_numberless=map(
lambda n:n.replace('div','').strip('1234567890_'),self.labnam)
node_groups=map(lambda n:n[3:],self.node_labels_numberless)
#put group names in ordered set
n_set=set()
self.group_labels=(
[i for i in node_groups if i not in n_set and not n_set.add(i)])
self.nr_groups=len(self.group_labels)
#get map of {node name -> node group}
grp_ids=dict(zip(self.group_labels,xrange(self.nr_groups)))
#group colors does not change unless the parcellation is reloaded
self.group_colors=(
[hi_contrast_cmap(i/self.nr_groups) for i in range(self.nr_groups)])
#node colors changes constantly, so copy and stash the result
self.node_colors=map(lambda n:self.group_colors[grp_ids[n]],node_groups)
self.node_colors_default=list(self.node_colors)
#create the color legend associated with this dataset
def create_color_legend_entry(zipped):
label,color=zipped
return LegendEntry(metaregion=label,col=color)
self.color_legend.entries=map(create_color_legend_entry,
zip(self.group_labels,self.group_colors))
#set up some colors that are acceptably high contrast for modules
#this is unrelated to node colors in any way, for multi-module mode
self.module_colors=(
[[255,255,255,255],[204,0,0,255],[51,204,51,255],[66,0,204,255],
[80,230,230,255],[51,153,255,255],[255,181,255,255],
[255,163,71,255],[221,221,149,255],[183,230,46,255],
[77,219,184,255],[255,255,204,255],[0,0,204,255],[204,69,153,255],
[255,255,0,255],[0,128,0,255],[163,117,25,255],[255,25,117,255]])
######################################################################
# DRAW METHODS
######################################################################
def draw(self):
self.draw_surfs(); self.draw_nodes(); self.draw_conns()
def draw_surfs(self):
for data_view in (self.dv_3d, self.dv_mat, self.dv_circ):
data_view.draw_surfs()
def draw_nodes(self):
self.set_node_colors()
for data_view in (self.dv_3d, self.dv_mat, self.dv_circ):
data_view.draw_nodes()
def set_node_colors(self):
#set node_colors
if self.display_mode=='normal':
self.node_colors=list(self.node_colors_default)
elif self.display_mode=='scalar':
#node colors are not used here, instead the scalar value is set directly
self.node_colors=list(self.node_colors_default)
elif self.display_mode=='module_single':
new_colors=np.tile(.3,self.nr_labels)
new_colors[self.get_module()]=.8
self.node_colors=list(self.opts.default_map._pl(new_colors))
elif self.display_mode=='module_multi':
while self.nr_modules > len(self.module_colors):
i,j=np.random.randint(18,size=(2,))
col=(np.array(self.module_colors[i])+self.module_colors[j])/2
col=np.array(col,dtype=int)
self.module_colors.append(col.tolist())
perm=np.random.permutation(len(self.module_colors))
#mayavi scalars depend on saving the module colors
self.module_colors=np.array(self.module_colors)[perm].tolist()
cols=self.module_colors[:self.nr_modules]
import bct
ci=bct.ls2ci(self.modules,zeroindexed=True)
self.node_colors=((np.array(self.module_colors)[ci])/255).tolist()
def draw_conns(self,conservative=False):
if conservative: new_edges = None
else: new_edges,count_edges = self.select_conns()
for data_view in (self.dv_3d, self.dv_mat, self.dv_circ):
if data_view is not None:
data_view.draw_conns(new_edges)
def select_conns(self):
lo=self.thresval
hi=np.max(self.adjdat)
basic_conds=lambda e,a,b:(not self.masked[e] and
self.curr_node is None or self.curr_node in (a,b))
if self.display_mode=='module_single':
#find the right module
module=self.get_module()
#attach the right conditions
if self.opts.module_view_style=='intramodular':
conds = lambda e,a,b:(basic_conds(e,a,b) and
(a in module and b in module))
elif self.opts.module_view_style=='intermodular':
conds = lambda e,a,b:(basic_conds(e,a,b) and
((a in module) != (b in module))) #xor
elif self.opts.module_view_stlye=='both':
conds = lambda e,a,b:(basic_conds(e,a,b) and
(a in module or b in module))
else:
conds=basic_conds
new_edges=np.zeros((self.nr_edges,2),dtype=int)
count_edges=0
for e,(a,b) in enumerate(zip(self.edges[:,0],self.edges[:,1])):
if conds(e,a,b):
new_edges[e]=(a,b)
#do the threshold checking here. This code breaks the
#design spec; the dataset is checking the dataview and
#messing with its internals. obviously, the reason why
#is that this code runs often and needs to be optimized
if self.dv_circ is not None and not self.opts.disable_circle:
ev=self.adjdat[e]
if (lo <= ev <= hi):
self.dv_circ.circ_data[e].set_visible(True)
ec=self.opts.activation_map._pl((ev-lo)/(hi-lo))
self.dv_circ.circ_data[e].set_ec(ec)
count_edges+=1
else:
self.dv_circ.circ_data[e].set_visible(False)
else:
new_edges[e]=(0,0)
if self.dv_circ is not None and not self.opts.disable_circle:
self.dv_circ.circ_data[e].set_visible(False)
return new_edges,count_edges
def center_adjmat(self):
self.dv_mat.center()
######################################################################
# I/O METHODS (LOADING, SAVING)
######################################################################
def load_parc(self,lab_pos,labnam,srf,labv):
self.lab_pos=lab_pos
self.labnam=labnam
self.srf=srf
self.labv=labv
self.nr_labels=len(labnam)
#there is no need to call pos_helper_gen here! pos_helper_gen
#only has to do with edges. previously it also reset scalars, but we
#don't do that in load_adj so there is no reason for it
#self.pos_helper_gen()
self.node_scalars = {}
self.color_legend=ColorLegend()
self.node_colors_gen()
self.adj=None #whatever adj was before, it is now the wrong size
self.reset_dataviews()
def load_adj(self,adj,soft_max_edges,reqrois,suppress_extra_rois):
self.adj=adj
self.soft_max_edges=soft_max_edges
#it is necessary to rerun pos_helper_gen() on every load because the
#number of edges
#is not constant from one adjmat to another and which edges are thrown
#away under the soft cap may differ. pos_helper_gen is really all about
#edge positions. we wouldnt have to do this if *all* previously
#subcutoff are still subcutoff (which is unlikely).
#we could also potentially avoid having to do this if we knew that the
#parcellation didnt change and only contained nr_edges < soft_max. But
#its not worth bothering
self.pos_helper_gen()
#flip adj ord should already be done to the preprocessed adj
self.adj_helper_gen()
self.dv_3d.vectors_clear()
self.display_mode='normal'
self.dv_3d.supply_adj()
self.dv_mat.supply_adj()
self.dv_circ.supply_adj(reqrois=reqrois, suppress_extra_rois=suppress_extra_rois)
self.display_all()
#This method takes a TractographyChooserParameters
def load_tractography(self,params):
if not params.track_file:
self.error_dialog('You must specify a valid tractography file'); return
if not params.b0_volume:
self.error_dialog('You must specify a B0 volume from which the registration'
' to the diffusion space can be computed'); return
if not params.subjects_dir or not params.subject:
self.error_dialog('You must specify the freesurfer reconstruction for the '
'individual subject for registration to the surface space.'); return
self.dv_3d.tracks_gen(params)
#This method takes a GeneralMatrixChooserParameters
def load_modules_or_scalars(self,params):
if not params.mat:
self.error_dialog('You must specify a valid matrix file'); return
if params.whichkind=='scalars' and not params.measure_name:
self.error_dialog('Cannot leave scalar name blank. cvu uses '
'this value as a dictionary index'); return
import preprocessing
try:
ci=preprocessing.loadmat(params.mat, field=params.field_name)
except (CVUError,IOError) as e: self.error_dialog(str(e)); return
if params.mat_order:
try:
init_ord, bads = preprocessing.read_ordering_file(
params.mat_order)
except (IndexError,UnicodeDecodeError) as e:
self.error_dialog(str(e)); return
#delete the bads
if not params.ignore_deletes:
ci=np.delete(ci,bads)
#perform the swapping
try:
ci_ord = preprocessing.adj_sort(init_ord, self.labnam)
except CVUError as e: self.error_dialog(str(e)); return
except KeyError as e:
self.error_dialog('Field not found: %s'%str(e)); return
ci=ci[ci_ord]
try:
ci=np.reshape(ci,(self.nr_labels,))
except ValueError as e:
self.error_dialog('The %s file is of size %i after deletions, but '
'the dataset has %i regions' %
(params.whichkind, len(ci), self.nr_labels)); return
if params.whichkind=='modules':
import bct
self.modules=bct.ci2ls(ci)
self.nr_modules=len(self.modules)
elif params.whichkind=='scalars':
self.save_scalar(params.measure_name,ci)
params._dataset_plusplus()
#this method destroys the current dataviews and resets them entirely
def reset_dataviews(self):
#in principle it might be useful to do some more cleanup here
self.display_mode='normal'
self.dv_3d=DVMayavi(self)
self.dv_mat=DVMatrix(self)
self.dv_circ=DVCircle(self)
self.chg_scalar_colorbar()
#scalar colorbar loading is tied to the surface and not to nodes
#because the surface always has the same color scheme and the nodes
#don't. but it can't be in surfs_gen because the surf can get gen'd
#when switching from cracked to glass. so it is here.
#handles the scaling and size checking for new scalar datasets
def save_scalar(self,name,scalars,passive=False):
if np.squeeze(scalars).shape != (self.nr_labels,):
if passive:
self.verbose_msg("%s: Only Nx1 vectors can be saved as scalars"%name)
return
else:
self.error_dialog("%s: Only Nx1 vectors can be saved as scalars"%name)
#print np.squeeze(scalars).shape, self.nr_labels
return
ci=scalars.ravel().copy()
ci=(ci-np.min(ci))/(np.max(ci)-np.min(ci))
self.node_scalars.update({name:ci})
#this function takes a SnapshotParameters object and returns a
#continuation -- a closure -- which saves the snapshot. The CVU object
#spawns the "Really overwrite file" window if the file exists, and then
#calls the continuation, or else just calls the continuation directly.
def snapshot(self,params):
def save_continuation():
try:
if params.whichplot=='3D brain':
self.dv_3d.snapshot(params)
elif params.whichplot=='connection matrix':
self.dv_mat.snapshot(params)
elif params.whichplot=='circle plot':
self.dv_circ.snapshot(params)
except IOError as e:
self.error_dialog(str(e))
except KeyError as e:
self.error_dialog('The library making the snapshot supports'
' multiple file types and doesnt know which one you want.'
' Please specify a file extension to disambiguate.')
return save_continuation
#this function takes a MakeMovieParameters object and returns a
#continuation which records and takes the movie. The CVU object is again
#responsible for thinking about the "really overwrite file" case.
def make_movie(self,params):
def save_continuation(): self.dv_3d.make_movie(params)
return save_continuation
def make_movie_finish(self,params):
self.dv_3d.make_movie_finish(params)
######################################################################
# VISUALIZATION INTERACTIONS
######################################################################
def display_all(self):
self.display_mode='normal'
self.curr_node=None
self.cur_module=None
self.center_adjmat()
self.draw()
def display_node(self,n):
if n<0 or n>=self.nr_labels: return
self.curr_node=n
self.draw_conns()
def display_scalars(self):
self.display_mode='scalar'
self.draw_surfs()
self.draw_nodes()
def display_module(self,module):
self.display_mode='module_single'
self.curr_node=None
self.cur_module=module
self.draw() #draw surf is needed to unset surf color
def display_multi_module(self):
if not self.modules:
self.error_dialog('No modules defined')
return
self.display_mode='module_multi'
self.draw_nodes()
def calculate_modules(self,thres):
import bct
thres_adj=self.adj.copy()
thres_adj[thres_adj < thres] = 0
self.verbose_msg('Threshold for modularity calculation: %s'%str(thres))
modvec,_=bct.modularity_und(thres_adj)
self.modules = bct.ci2ls(modvec)
self.nr_modules = len(self.modules)
def calculate_graph_stats(self,thres):
import graph,bct
thres_adj = self.adj.copy()
thres_adj[thres_adj < thres] = 0
self.verbose_msg('Threshold for graph calculations: %s'%str(thres))
try:
self.graph_stats=graph.do_summary(thres_adj,bct.ls2ci(self.modules),
self.opts.intermediate_graphopts_list)
for name,arr in self.graph_stats.iteritems():
self.save_scalar(name,arr,passive=True)
except CVUError:
self.error_dialog("Community structure required for some of "
"the calculations specified. Try calculating modules first.")
#save_graphstat_to_scalar is principally interaction between window and
#dataset manager. it should call a generic save_scalar method, same as
#loading a scalar from a file
######################################################################
# OPTIONS
######################################################################
def prop_thresh(self):
try:
self.thresval=float(self.adjdat[
int(round(self.opts.pthresh*self.nr_edges-1))])
except TraitError as e:
if self.opts.pthresh>1:
self.warning_dialog("%s\nThreshold set to maximum"%str(e))
elif self.opts.pthresh<0:
self.warning_dialog("%s\nThreshold set to minimum"%str(e))
else:
self.error_dialog("Programming error")
def abs_thresh(self):
self.thresval=self.opts.athresh
if self.adjdat[self.nr_edges-1] < self.opts.athresh:
self.thresval=self.adjdat[self.nr_edges-1]
self.warning_dialog("Threshold over maximum! Set to maximum.")
elif self.adjdat[0] > self.opts.athresh:
self.thresval=self.adjdat[0]
self.warning_dialog("Threshold under minimum! Set to minimum.")
#recall reset thresh is a cached property
@on_trait_change('opts:pthresh')
def chg_pthresh_val(self):
if self.opts.thresh_type != 'prop': return
self.reset_thresh()
self.draw_conns(conservative=True)
@on_trait_change('opts:athresh')
def chg_athresh_val(self):
if self.opts.thresh_type != 'abs': return
self.reset_thresh()
self.draw_conns(conservative=True)
@on_trait_change('opts:thresh_type')
def chg_thresh_type(self):
self.draw_conns(conservative=True)
@on_trait_change('opts:interhemi_conns_on')
def chg_interhemi_connmask(self):
self.masked[self.interhemi]=not self.opts.interhemi_conns_on
@on_trait_change('opts:lh_conns_on')
def chg_lh_connmask(self):
self.masked[self.left]=not self.opts.lh_conns_on
@on_trait_change('opts:rh_conns_on')
def chg_rh_connmask(self):
self.masked[self.right]=not self.opts.rh_conns_on
#the following options operate on specific views only
#they may fail if the view is not present (i dont know if this is true)
@on_trait_change('opts:circ_size')
def chg_circ_size(self):
try:
self.dv_circ.circ.axes[0].set_ylim(0,self.opts.circ_size)
self.dv_circ.circ.canvas.draw()
except AttributeError: pass
@on_trait_change('opts:show_floating_text')
def chg_float_text(self):
try: self.dv_3d.txt.visible=self.opts.show_floating_text
except AttributeError: pass
@on_trait_change('opts:scalar_colorbar')
def chg_scalar_colorbar(self):
try: self.dv_3d.set_colorbar(self.opts.scalar_colorbar,
self.dv_3d.syrf_lh, orientation='vertical')
except AttributeError: pass
@on_trait_change('opts:render_style')
def chg_render_style(self):
try: self.dv_3d.set_surf_render_style(self.opts.render_style)
except AttributeError: pass
@on_trait_change('opts:surface_visibility')
def chg_surf_opacity(self):
try:
for syrf in (self.dv_3d.syrf_lh, self.dv_3d.syrf_rh):
syrf.actor.property.opacity=self.opts.surface_visibility
except AttributeError: pass
@on_trait_change('opts:lh_nodes_on')
def chg_lh_nodemask(self):
try: self.dv_3d.nodes_lh.visible=self.opts.lh_nodes_on
except AttributeError: pass
@on_trait_change('opts:rh_nodes_on')
def chg_rh_nodemask(self):
try: self.dv_3d.nodes_rh.visible=self.opts.rh_nodes_on
except AttributeError: pass
@on_trait_change('opts:lh_surfs_on')
def chg_lh_surfmask(self):
try: self.dv_3d.syrf_lh.visible=self.opts.lh_surfs_on
except AttributeError: pass
@on_trait_change('opts:rh_surfs_on')
def chg_rh_surfmask(self):
try: self.dv_3d.syrf_rh.visible=self.opts.rh_surfs_on
except AttributeError: pass
@on_trait_change('opts:conns_colors_on')
def chg_conns_colors(self):
try:
if self.opts.conns_colors_on:
self.dv_3d.vectors.glyph.color_mode='color_by_scalar'
else:
self.dv_3d.vectors.glyph.color_mode='no_coloring'
except AttributeError: pass
@on_trait_change('opts:conns_colorbar')
def chg_conns_colorbar(self):
try: self.dv_3d.set_colorbar(self.opts.conns_colorbar,
self.dv_3d.vectors, orientation='horizontal')
except AttributeError: pass
@on_trait_change('opts:conns_width')
def chg_conns_width(self):
try: self.dv_3d.vectors.actor.property.line_width=self.opts.conns_width
except AttributeError: pass
@on_trait_change('opts:default_map.[cmap,reverse,fname,threshold]')
def chg_default_map(self):
try:
self.draw_nodes()
except:
map_def = self.opts.default_map
if map_def.cmap == 'file' and not map_def.fname:
pass
else:
raise
@on_trait_change('opts:scalar_map.[cmap,reverse,fname,threshold]')
def chg_scalar_map(self):
try:
self.draw_surfs()
self.draw_nodes()
except:
map_sca = self.opts.scalar_map
if map_sca.cmap == 'file' and not map_sca.fname:
pass
else:
raise
@on_trait_change('opts:activation_map.[cmap,reverse,fname,threshold]')
def chg_activation_map(self):
#we don't touch the circle plot here since circle redraw is expensive
try:
self.dv_3d.draw_conns()
except:
map_act = self.opts.activation_map
if map_act.cmap == 'file' and not map_act.fname:
pass
else:
raise
@on_trait_change('opts:connmat_map.[cmap,reverse,fname,threshold]')
def chg_connmat_map(self):
try:
self.dv_mat.change_colormap()
except:
map_mat = self.opts.connmat_map
if map_mat.cmap == 'file' and not map_mat.fname:
pass
else:
raise
######################################################################
# MISCELLANEOUS HELPERS
######################################################################
def error_dialog(self,str):
return self.gui.error_dialog(str)
def warning_dialog(self,str):
return self.gui.warning_dialog(str)
def verbose_msg(self,str):
return self.gui.verbose_msg(str)
def get_module(self):
if self.cur_module=='custom':
return self.custom_module
elif isinstance(self.cur_module,int) and self.modules is not None:
return self.modules[self.cur_module]
class Dataset(HasTraits):
########################################################################
# FUNDAMENTALLY NECESSARY DATA
########################################################################
name = Str #give this dataset a name
gui = Any #symbolic reference to a modular cvu
nr_labels = Int
nr_edges = Int
labnam = List(Str)
#adjlabfile=File
#the adjlabfile is not needed. this is only kept on hand to pass it
#around if specified as CLI arg. it is only used upon loading an adjmat.
#so just have adjmat loading be a part of dataset creation and get rid of
#keeping track of this
adj = Any #NxN np.ndarray
adj_thresdiag = Property(depends_on='adj') #NxN np.ndarray
@cached_property
def _get_adj_thresdiag(self):
adjt = self.adj.copy()
adjt[np.where(np.eye(self.nr_labels))] = np.min(adjt[np.where(adjt)])
return adjt
starts = Any #Ex3 np.ndarray
vecs = Any #Ex3 np.ndarray
edges = Any #Ex2 np.ndarray(int)
srf = Instance(SurfData)
#labv=List(Instance(mne.Label))
#all that is needed from this is a map of name->vertex
#this is a considerable portion of the data contained in a label but still
#only perhaps 15%. To make lightweight, extract this from labv
#TODO convert it to that
labv = Dict #is an OrderedDict in parcellation order
lab_pos = Any #Nx3 np.ndarray
#########################################################################
# CRITICAL NONADJUSTABLE DATA WITHOUT WHICH DISPLAY CANNOT EXIST
#########################################################################
dv_3d = Either(Instance(DataView), None)
dv_mat = Either(Instance(DataView), None)
dv_circ = Either(Instance(DataView), None)
soft_max_edges = Int
adjdat = Any #Ex1 np.ndarray
left = Any #Nx1 np.ndarray(bool)
right = Any #Nx1 np.ndarray(bool)
interhemi = Any #Nx1 np.ndarray(bool)
masked = Any #Nx1 np.ndarray(bool)
lhnodes = Property(depends_on='labnam') #Nx1 np.ndarray(int)
rhnodes = Property(depends_on='labnam') #Nx1 np.ndarray(int)
@cached_property
def _get_lhnodes(self):
return np.where(map(lambda r: r[0] == 'l', self.labnam))[0]
@cached_property
def _get_rhnodes(self):
return np.where(map(lambda r: r[0] == 'r', self.labnam))[0]
node_colors = Any #Nx3 np.ndarray
#node_colors represents the colors held by the nodes. the current value of
#node_colors depends on the current policy (i.e. the current display mode).
#however, don't take this all too literally. depending on the current
#policy, the dataviews may choose to ignore what is in node_colors and
#use some different color.
#this is always true of Mayavi views, who can't use the node colors at all.
#because mayavi doesn't play nice with true colors (this could be fixed
#if mayavi is fixed). it is also true of the other plots in scalar mode,
#but when scalars are not specified for those dataviews.
#node_colors_default will be set uniquely for each parcellation and can
#thus be different for different datasets.
#group_colors is more subtle; it can in principle be set uniquely for each
#parcellation as long as the parcellations don't conform to aparc. for
#instance, destrieux parc has different group colors. right now i'm a long
#way away from dealing with this but i think in a month it will be prudent
#to just have the dataset capture both of these variables
node_colors_default = List
node_labels_numberless = List(Str)
group_colors = List
nr_groups = Int
group_labels = List(Str)
color_legend = Instance(ColorLegend)
module_colors = List
default_glass_brain_color = Constant((.82, .82, .82))
#########################################################################
# ASSOCIATED STATISTICAL AND ANALYTICAL DATA
#########################################################################
node_scalars = Dict
scalar_display_settings = Instance(ScalarDisplaySettings)
#TODO make modules a dictionary
modules = List
nr_modules = Int
graph_stats = Dict
#########################################################################
# ASSOCIATED DISPLAY OPTIONS AND DISPLAY STATE (ADJUSTABLE/TRANSIENT)
#########################################################################
opts = Instance(DisplayOptions)
display_mode = Enum('normal', 'scalar', 'module_single', 'module_multi')
reset_thresh = Property(Method)
def _get_reset_thresh(self):
if self.opts.thresh_type == 'prop': return self.prop_thresh
elif self.opts.thresh_type == 'abs': return self.abs_thresh
thresval = Float
curr_node = Either(Int, None)
cur_module = Either(Int, 'custom', None)
custom_module = List
########################################################################
# SETUP
########################################################################
def __init__(self,
name,
lab_pos,
labnam,
srf,
labv,
gui=None,
adj=None,
soft_max_edges=20000,
**kwargs):
super(Dataset, self).__init__(**kwargs)
self.gui = gui
self.name = name
self.opts = DisplayOptions(self)
self.scalar_display_settings = ScalarDisplaySettings(self)
#this is effectively load_parc
self.lab_pos = lab_pos
self.labnam = labnam
self.srf = srf
self.labv = labv
self.nr_labels = len(labnam)
#load_parc redundantly sets the current display but oh well.
#self.load_parc(lab_pos,labnam,srf,labv,
# init_display.subject_name,init_display.parc_name)
#if adj is None, it means it will be guaranteed to be supplied later
#by the user
#this is load adj, except without initializing nonexistent dataviews
if adj is not None:
self.adj = adj
self.soft_max_edges = soft_max_edges
self.pos_helper_gen()
#flip adj ord should already be done to the preprocessed adj
self.adj_helper_gen()
self.color_legend = ColorLegend()
self.node_colors_gen()
self.dv_3d = DVMayavi(self)
self.dv_mat = DVMatrix(self)
self.dv_circ = DVCircle(self)
self.chg_scalar_colorbar()
def __repr__(self):
return 'Dataset: %s' % self.name
def __getitem__(self, key):
if key == 0: return self
elif key == 1: return self.name
else:
raise KeyError(
'Invalid indexing to dataset. Dataset indexing '
'is implemented to appease CheckListEditor and can only be 0 or 1.'
)
########################################################################
# GEN METHODS
########################################################################
#preconditions: lab_pos has been set.
def pos_helper_gen(self, reset_scalars=True):
self.nr_labels = n = len(self.lab_pos)
self.nr_edges = self.nr_labels * (self.nr_labels - 1) // 2
#self.starts = np.zeros((self.nr_edges,3),dtype=float)
#self.vecs = np.zeros((self.nr_edges,3),dtype=float)
#self.edges = np.zeros((self.nr_edges,2),dtype=int)
#i=0
#for r2 in xrange(0,self.nr_labels,1):
# for r1 in xrange(0,r2,1):
#self.starts[i,:] = self.lab_pos[r1]
#self.vecs[i,:] = self.lab_pos[r2]-self.lab_pos[r1]
#self.edges[i,0],self.edges[i,1] = r1,r2
#i+=1
tri_ixes = np.triu(np.ones((n, n)), 1)
ixes, = np.where(tri_ixes.flat)
A_r = np.tile(self.lab_pos, (n, 1, 1))
self.starts = np.reshape(A_r, (n * n, 3))[ixes, :]
self.vecs = np.reshape(A_r - np.transpose(A_r, (1, 0, 2)),
(n * n, 3))[ixes, :]
self.edges = np.transpose(np.where(tri_ixes.T))[:, ::-1]
#pos_helper_gen is now only called from load adj. The reason it is
#because it can change on all adj changes because of the soft
#cap. The number of edges can differ between adjmats because of the
#soft cap and all of the positions need to be recalculated if it does.
#pos_helper_gen really only has to do with edge positions. Node and
#surf positions dont depend on it at all.
#TODO possibly, keep track of the soft cap and do nothing if it hasn't
#changed
#RESPONSE: yes but this check should be done in adj_load
if reset_scalars:
self.node_scalars = {}
self.display_mode = 'normal'
#precondition: adj_helper_gen() must be run after pos_helper_gen()
def adj_helper_gen(self):
self.nr_edges = self.nr_labels * (self.nr_labels - 1) // 2
self.adjdat = np.zeros((self.nr_edges), dtype=float)
self.interhemi = np.zeros((self.nr_edges), dtype=bool)
self.left = np.zeros((self.nr_edges), dtype=bool)
self.right = np.zeros((self.nr_edges), dtype=bool)
self.masked = np.zeros((self.nr_edges), dtype=bool)
i = 0
self.adj[xrange(self.nr_labels), xrange(self.nr_labels)] = 0
#for r2 in xrange(0,self.nr_labels,1):
#self.adj[r2][r2]=0
#for r1 in xrange(0,r2,1):
#self.adjdat[i] = self.adj[r1][r2]
#self.interhemi[i] = self.labnam[r1][0] != self.labnam[r2][0]
#self.left[i] = self.labnam[r1][0]==self.labnam[r2][0]=='l'
#self.right[i] = self.labnam[r1][0]==self.labnam[r2][0]=='r'
#i+=1
n = self.nr_labels
ixes, = np.where(np.triu(np.ones((n, n)), 1).flat)
self.adjdat = self.adj.flat[::-1][ixes][::-1]
from parsing_utils import same_hemi
sh = np.vectorize(same_hemi)
L_r = np.tile(self.labnam, (self.nr_labels, 1))
self.interhemi = np.logical_not(sh(L_r, L_r.T)).flat[::-1][ixes][::-1]
self.left = sh(L_r, L_r.T, 'l').flat[::-1][ixes][::-1]
self.right = sh(L_r, L_r.T, 'r').flat[::-1][ixes][::-1]
#remove all but the soft_max_edges largest connections
if self.nr_edges > self.soft_max_edges:
cutoff = sorted(self.adjdat)[self.nr_edges - self.soft_max_edges -
1]
zi = np.where(self.adjdat >= cutoff)
# if way way too many edges remain, make it a hard max
# this happens in DTI data which is very sparse, the cutoff is 0
if len(zi[0]) > (self.soft_max_edges + 200):
zi = np.where(self.adjdat > cutoff)
self.starts = self.starts[zi[0], :]
self.vecs = self.vecs[zi[0], :]
self.edges = self.edges[zi[0], :]
self.adjdat = self.adjdat[zi[0]]
self.interhemi = self.interhemi[zi[0]]
self.left = self.left[zi[0]]
self.right = self.right[zi[0]]
self.nr_edges = len(self.adjdat)
self.verbose_msg(str(self.nr_edges) + " total connections")
#sort the adjdat
sort_idx = np.argsort(self.adjdat, axis=0)
self.adjdat = self.adjdat[sort_idx].squeeze()
self.edges = self.edges[sort_idx].squeeze()
self.starts = self.starts[sort_idx].squeeze()
self.vecs = self.vecs[sort_idx].squeeze()
self.left = self.left[sort_idx].squeeze()
self.right = self.right[sort_idx].squeeze()
self.interhemi = self.interhemi[sort_idx].squeeze()
self.masked = self.masked[sort_idx].squeeze() #just to prune
#try to auto-set the threshold to a reasonable value
if self.nr_edges < 500:
self.opts.pthresh = .01
else:
thr = (self.nr_edges - 500) / (self.nr_edges)
self.opts.pthresh = thr
self.opts.thresh_type = 'prop'
self.display_mode = 'normal'
def node_colors_gen(self):
#node groups could change upon loading a new parcellation
hi_contrast_clist = ('#26ed1a', '#eaf60b', '#e726f4', '#002aff',
'#05d5d5', '#f4a5e0', '#bbb27e', '#641179',
'#068c40')
hi_contrast_cmap = LinearSegmentedColormap.from_list(
'hi_contrast', hi_contrast_clist)
#labels are assumed to start with lh_ and rh_
self.node_labels_numberless = map(
lambda n: n.replace('div', '').strip('1234567890_'), self.labnam)
node_groups = map(lambda n: n[3:], self.node_labels_numberless)
#put group names in ordered set
#n_set=set()
#self.group_labels=(
# [i for i in node_groups if i not in n_set and not n_set.add(i)])
node_groups_hemi1 = map(
lambda n: n[3:], self.node_labels_numberless[:len(self.lhnodes)])
node_groups_hemi2 = map(
lambda n: n[3:], self.node_labels_numberless[-len(self.rhnodes):])
a_set = set()
self.group_labels = ([
i for i in node_groups_hemi1 if not i in a_set and not a_set.add(i)
])
last_grp = None
for grp in node_groups_hemi2:
if grp not in self.group_labels:
if last_grp is None:
self.group_labels.insert(grp, 0)
else:
self.group_labels.insert(
self.group_labels.index(last_grp) + 1, grp)
else:
last_grp = grp
self.nr_groups = len(self.group_labels)
#get map of {node name -> node group}
grp_ids = dict(zip(self.group_labels, xrange(self.nr_groups)))
#group colors does not change unless the parcellation is reloaded
self.group_colors = ([
hi_contrast_cmap(i / self.nr_groups) for i in range(self.nr_groups)
])
#node colors changes constantly, so copy and stash the result
self.node_colors = map(lambda n: self.group_colors[grp_ids[n]],
node_groups)
self.node_colors_default = list(self.node_colors)
#create the color legend associated with this dataset
def create_color_legend_entry(zipped):
label, color = zipped
return LegendEntry(metaregion=label, col=color)
self.color_legend.entries = map(
create_color_legend_entry, zip(self.group_labels,
self.group_colors))
#set up some colors that are acceptably high contrast for modules
#this is unrelated to node colors in any way, for multi-module mode
self.module_colors = ([[255, 255, 255, 255], [204, 0, 0, 255],
[51, 204, 51, 255], [66, 0, 204, 255],
[80, 230, 230, 255], [51, 153, 255, 255],
[255, 181, 255, 255], [255, 163, 71, 255],
[221, 221, 149, 255], [183, 230, 46, 255],
[77, 219, 184, 255], [255, 255, 204, 255],
[0, 0, 204, 255], [204, 69, 153, 255],
[255, 255, 0, 255], [0, 128, 0, 255],
[163, 117, 25, 255], [255, 25, 117, 255]])
######################################################################
# DRAW METHODS
######################################################################
def draw(self, skip_circ=False):
self.draw_surfs()
self.draw_nodes(skip_circ=skip_circ)
self.draw_conns(skip_circ=skip_circ)
def draw_surfs(self):
for data_view in (self.dv_3d, self.dv_mat, self.dv_circ):
data_view.draw_surfs()
def draw_nodes(self, skip_circ=False):
self.set_node_colors()
for data_view in (self.dv_3d, self.dv_mat, self.dv_circ):
if skip_circ and data_view is self.dv_circ:
continue
data_view.draw_nodes()
def set_node_colors(self):
#set node_colors
if self.display_mode == 'normal':
self.node_colors = list(self.node_colors_default)
elif self.display_mode == 'scalar':
#node colors are not used here, instead the scalar value is set directly
self.node_colors = list(self.node_colors_default)
elif self.display_mode == 'module_single':
new_colors = np.tile(.3, self.nr_labels)
new_colors[self.get_module()] = .8
self.node_colors = list(self.opts.default_map._pl(new_colors))
elif self.display_mode == 'module_multi':
while self.nr_modules > len(self.module_colors):
i, j = np.random.randint(18, size=(2, ))
col = (np.array(self.module_colors[i]) +
self.module_colors[j]) / 2
col = np.array(col, dtype=int)
self.module_colors.append(col.tolist())
#perm=np.random.permutation(len(self.module_colors))
#mayavi scalars depend on saving the module colors
#self.module_colors=np.array(self.module_colors)[perm].tolist()
cols = self.module_colors[:self.nr_modules]
import bct
ci = bct.ls2ci(self.modules, zeroindexed=True)
self.node_colors = ((np.array(self.module_colors)[ci]) /
255).tolist()
def draw_conns(self, conservative=False, skip_circ=False):
if conservative: new_edges = None
else: new_edges, count_edges = self.select_conns(skip_circ=skip_circ)
for data_view in (self.dv_3d, self.dv_mat, self.dv_circ):
if skip_circ and data_view is self.dv_circ:
continue
elif data_view is not None:
data_view.draw_conns(new_edges)
def select_conns(self, skip_circ=False):
disable_circle = (skip_circ or self.opts.circle_render == 'disabled')
lo = self.thresval
hi = np.max(self.adjdat)
basic_conds = lambda e, a, b: (not self.masked[e] and self.curr_node is
None or self.curr_node in (a, b))
if self.display_mode == 'module_single':
#find the right module
module = self.get_module()
#attach the right conditions
if self.opts.module_view_style == 'intramodular':
conds = lambda e, a, b: (basic_conds(e, a, b) and
(a in module and b in module))
elif self.opts.module_view_style == 'intermodular':
conds = lambda e, a, b: (basic_conds(e, a, b) and (
(a in module) != (b in module))) #xor
elif self.opts.module_view_stlye == 'both':
conds = lambda e, a, b: (basic_conds(e, a, b) and
(a in module or b in module))
else:
conds = basic_conds
new_edges = np.zeros((self.nr_edges, 2), dtype=int)
count_edges = 0
for e, (a, b) in enumerate(zip(self.edges[:, 0], self.edges[:, 1])):
if conds(e, a, b):
new_edges[e] = (a, b)
#do the threshold checking here. This code breaks the
#design spec; the dataset is checking the dataview and
#messing with its internals. obviously, the reason why
#is that this code runs often and needs to be optimized
if self.dv_circ is not None and not disable_circle:
ev = self.adjdat[e]
if (lo <= ev <= hi):
self.dv_circ.circ_data[e].set_visible(True)
ec = self.opts.activation_map._pl(
(ev - lo) / (hi - lo))
self.dv_circ.circ_data[e].set_ec(ec)
count_edges += 1
else:
self.dv_circ.circ_data[e].set_visible(False)
else:
new_edges[e] = (0, 0)
if self.dv_circ is not None and not disable_circle:
self.dv_circ.circ_data[e].set_visible(False)
return new_edges, count_edges
def center_adjmat(self):
self.dv_mat.center()
######################################################################
# I/O METHODS (LOADING, SAVING)
######################################################################
def _load_parc(self, lab_pos, labnam, srf, labv):
self.lab_pos = lab_pos
self.labnam = labnam
self.srf = srf
self.labv = labv
self.nr_labels = len(labnam)
#there is no need to call pos_helper_gen here! pos_helper_gen
#only has to do with edges. previously it also reset scalars, but we
#don't do that in load_adj so there is no reason for it
#self.pos_helper_gen()
self.node_scalars = {}
self.color_legend = ColorLegend()
self.node_colors_gen()
self.adj = None #whatever adj was before, it is now the wrong size
self.reset_dataviews()
def _load_adj(self, adj, soft_max_edges, reqrois, suppress_extra_rois):
self.adj = adj
self.soft_max_edges = soft_max_edges
#it is necessary to rerun pos_helper_gen() on every load because the
#number of edges
#is not constant from one adjmat to another and which edges are thrown
#away under the soft cap may differ. pos_helper_gen is really all about
#edge positions. we wouldnt have to do this if *all* previously
#subcutoff are still subcutoff (which is unlikely).
#we could also potentially avoid having to do this if we knew that the
#parcellation didnt change and only contained nr_edges < soft_max. But
#its not worth bothering
self.pos_helper_gen()
#flip adj ord should already be done to the preprocessed adj
self.adj_helper_gen()
self.dv_3d.vectors_clear()
self.display_mode = 'normal'
self.dv_3d.supply_adj()
self.dv_mat.supply_adj()
if self.opts.circle_render == 'asynchronous':
#first set up the 3D brain properly and then set the circle
#to generate itself in a background thread
self.display_all(skip_circ=True)
self.dv_3d.zaxis_view()
def threadsafe_circle_setup():
self.dv_circ.supply_adj(
reqrois=reqrois, suppress_extra_rois=suppress_extra_rois)
self.select_conns()
self.dv_circ.draw_conns()
Thread(target=threadsafe_circle_setup).start()
else:
#otherwise set up the circle and display everything in a
#single thread. If the circle is disabled this will not cause problems
self.dv_circ.supply_adj(reqrois=reqrois,
suppress_extra_rois=suppress_extra_rois)
self.display_all()
self.dv_3d.zaxis_view()
#This method takes a TractographyChooserParameters
def load_tractography(self, params):
if not params.track_file:
self.error_dialog('You must specify a valid tractography file')
return
if not params.b0_volume:
self.error_dialog(
'You must specify a B0 volume from which the '
'registration to the diffusion space can be computed')
return
if not params.subjects_dir or not params.subject:
self.error_dialog(
'You must specify the freesurfer reconstruction '
'for the individual subject for registration to the surface '
'space.')
return
self.dv_3d.tracks_gen(params)
#This method takes a GeneralMatrixChooserParameters
def load_modules_or_scalars(self, params):
if not params.mat:
self.error_dialog('You must specify a valid matrix file')
return
if params.whichkind == 'scalars' and not params.measure_name:
self.error_dialog('Cannot leave scalar name blank. cvu uses '
'this value as a dictionary index')
return
import preprocessing
try:
ci = preprocessing.loadmat(params.mat,
field=params.field_name,
is_adjmat=False)
except (CVUError, IOError) as e:
self.error_dialog(str(e))
return
if params.mat_order:
try:
init_ord, bads = preprocessing.read_ordering_file(
params.mat_order)
except (IndexError, UnicodeDecodeError) as e:
self.error_dialog(str(e))
return
#delete the bads
if not params.ignore_deletes:
ci = np.delete(ci, bads)
#perform the swapping
try:
ci_ord = preprocessing.adj_sort(init_ord, self.labnam)
except CVUError as e:
self.error_dialog(str(e))
return
except KeyError as e:
self.error_dialog('Field not found: %s' % str(e))
return
ci = ci[ci_ord]
try:
ci = np.reshape(ci, (self.nr_labels, ))
except ValueError as e:
self.error_dialog('The %s file is of size %i after deletions, but '
'the dataset has %i regions' %
(params.whichkind, len(ci), self.nr_labels))
return
if params.whichkind == 'modules':
import bct
self.modules = bct.ci2ls(ci)
self.nr_modules = len(self.modules)
elif params.whichkind == 'scalars':
self.save_scalar(params.measure_name, ci)
params._increment_scalar_count()
#this method destroys the current dataviews and resets them entirely
def reset_dataviews(self):
#in principle it might be useful to do some more cleanup here
self.display_mode = 'normal'
self.dv_3d = DVMayavi(self)
self.dv_mat = DVMatrix(self)
self.dv_circ = DVCircle(self)
self.chg_scalar_colorbar()
#scalar colorbar loading is tied to the surface and not to nodes
#because the surface always has the same color scheme and the nodes
#don't. but it can't be in surfs_gen because the surf can get gen'd
#when switching from cracked to glass. so it is here.
#handles the scaling and size checking for new scalar datasets
def save_scalar(self, name, scalars, passive=False):
if np.squeeze(scalars).shape != (self.nr_labels, ):
if passive:
self.verbose_msg(
"%s: Only Nx1 vectors can be saved as scalars" % name)
return
else:
self.error_dialog(
"%s: Only Nx1 vectors can be saved as scalars" % name)
#print np.squeeze(scalars).shape, self.nr_labels
return
ci = scalars.ravel().copy()
#ci=(ci-np.min(ci))/(np.max(ci)-np.min(ci))
self.node_scalars.update({name: ci})
#this function takes a SnapshotParameters object and returns a
#continuation -- a closure -- which saves the snapshot. The CVU object
#spawns the "Really overwrite file" window if the file exists, and then
#calls the continuation, or else just calls the continuation directly.
def snapshot(self, params):
def save_continuation():
try:
if params.whichplot == '3D brain':
self.dv_3d.snapshot(params)
elif params.whichplot == 'connection matrix':
self.dv_mat.snapshot(params)
elif params.whichplot == 'circle plot':
self.dv_circ.snapshot(params)
except IOError as e:
self.error_dialog(str(e))
except KeyError as e:
self.error_dialog(
'The library making the snapshot supports'
' multiple file types and doesnt know which one you want.'
' Please specify a file extension to disambiguate.')
return save_continuation
#this function takes a MakeMovieParameters object and returns a
#continuation which records and takes the movie. The CVU object is again
#responsible for thinking about the "really overwrite file" case.
def make_movie(self, params):
def save_continuation():
self.dv_3d.make_movie(params)
return save_continuation
def make_movie_finish(self, params):
self.dv_3d.make_movie_finish(params)
######################################################################
# VISUALIZATION INTERACTIONS
######################################################################
def display_all(self, skip_circ=False):
self.display_mode = 'normal'
self.curr_node = None
self.cur_module = None
self.center_adjmat()
self.draw(skip_circ=skip_circ)
def display_node(self, n):
if n < 0 or n >= self.nr_labels: return
self.curr_node = n
self.draw_conns()
def display_scalars(self):
self.display_mode = 'scalar'
self.draw_surfs()
self.draw_nodes()
def display_module(self, module):
self.display_mode = 'module_single'
self.curr_node = None
self.cur_module = module
self.draw() #draw surf is needed to unset surf color
def display_multi_module(self):
if not self.modules:
self.error_dialog('No modules defined')
return
self.display_mode = 'module_multi'
self.draw_surfs()
self.draw_nodes()
def calculate_modules(self, thres):
import graph, bct
thres_adj = self.adj.copy()
thres_adj[thres_adj < thres] = 0
self.verbose_msg('Threshold for modularity calculation: %s' %
str(thres))
modvec = graph.calculate_modules(thres_adj)
self.modules = bct.ci2ls(modvec)
self.nr_modules = len(self.modules)
def calculate_graph_stats(self, thres):
import graph, bct
thres_adj = self.adj.copy()
thres_adj[thres_adj < thres] = 0
self.verbose_msg('Threshold for graph calculations: %s' % str(thres))
try:
self.graph_stats = graph.do_summary(
thres_adj, bct.ls2ci(self.modules),
self.opts.intermediate_graphopts_list)
for name, arr in self.graph_stats.iteritems():
self.save_scalar(name, arr, passive=True)
except CVUError:
self.error_dialog(
"Community structure required for some of "
"the calculations specified. Try calculating modules first.")
#save_graphstat_to_scalar is principally interaction between window and
#dataset manager. it should call a generic save_scalar method, same as
#loading a scalar from a file
######################################################################
# OPTIONS
######################################################################
def prop_thresh(self):
try:
if int(round(self.opts.pthresh * self.nr_edges - 1)) < 0:
self.thresval = float(self.adjdat[0])
else:
self.thresval = float(self.adjdat[int(
round(self.opts.pthresh * self.nr_edges - 1))])
except TraitError as e:
if self.opts.pthresh > 1:
self.warning_dialog("%s\nThreshold set to maximum" % str(e))
elif self.opts.pthresh < 0:
self.warning_dialog("%s\nThreshold set to minimum" % str(e))
else:
self.error_dialog("Programming error")
def abs_thresh(self):
self.thresval = self.opts.athresh
if self.adjdat[self.nr_edges - 1] < self.opts.athresh:
self.thresval = self.adjdat[self.nr_edges - 1]
self.warning_dialog("Threshold over maximum! Set to maximum.")
elif self.adjdat[0] > self.opts.athresh:
self.thresval = self.adjdat[0]
self.warning_dialog("Threshold under minimum! Set to minimum.")
#recall reset thresh is a cached property
@on_trait_change('opts:pthresh')
def chg_pthresh_val(self):
if self.opts.thresh_type != 'prop': return
self.reset_thresh()
self.draw_conns(conservative=True)
@on_trait_change('opts:athresh')
def chg_athresh_val(self):
if self.opts.thresh_type != 'abs': return
self.reset_thresh()
self.draw_conns(conservative=True)
@on_trait_change('opts:thresh_type')
def chg_thresh_type(self):
self.draw_conns(conservative=True)
@on_trait_change('opts:interhemi_conns_on')
def chg_interhemi_connmask(self):
self.masked[self.interhemi] = not self.opts.interhemi_conns_on
@on_trait_change('opts:lh_conns_on')
def chg_lh_connmask(self):
self.masked[self.left] = not self.opts.lh_conns_on
@on_trait_change('opts:rh_conns_on')
def chg_rh_connmask(self):
self.masked[self.right] = not self.opts.rh_conns_on
#the following options operate on specific views only
#they may fail if the view is not present (i dont know if this is true)
@on_trait_change('opts:tube_conns')
def chg_tube_conns(self):
try:
self.dv_3d.set_tubular_properties()
except AttributeError:
pass
@on_trait_change('opts:circ_size')
def chg_circ_size(self):
try:
self.dv_circ.circ.axes[0].set_ylim(0, self.opts.circ_size)
self.dv_circ.circ.canvas.draw()
except AttributeError:
pass
@on_trait_change('opts:show_floating_text')
def chg_float_text(self):
try:
self.dv_3d.txt.visible = self.opts.show_floating_text
except AttributeError:
pass
@on_trait_change('opts:scalar_colorbar')
def chg_scalar_colorbar(self):
try:
self.dv_3d.set_colorbar(self.opts.scalar_colorbar,
self.dv_3d.syrf_lh,
orientation='vertical')
except AttributeError:
pass
@on_trait_change('opts:render_style')
def chg_render_style(self):
try:
self.dv_3d.set_surf_render_style(self.opts.render_style)
except AttributeError:
pass
@on_trait_change('opts:surface_visibility')
def chg_surf_opacity(self):
try:
for syrf in (self.dv_3d.syrf_lh, self.dv_3d.syrf_rh):
syrf.actor.property.opacity = self.opts.surface_visibility
except AttributeError:
pass
@on_trait_change('opts:lh_nodes_on')
def chg_lh_nodemask(self):
try:
self.dv_3d.nodes_lh.visible = self.opts.lh_nodes_on
except AttributeError:
pass
@on_trait_change('opts:rh_nodes_on')
def chg_rh_nodemask(self):
try:
self.dv_3d.nodes_rh.visible = self.opts.rh_nodes_on
except AttributeError:
pass
@on_trait_change('opts:lh_surfs_on')
def chg_lh_surfmask(self):
try:
self.dv_3d.syrf_lh.visible = self.opts.lh_surfs_on
except AttributeError:
pass
@on_trait_change('opts:rh_surfs_on')
def chg_rh_surfmask(self):
try:
self.dv_3d.syrf_rh.visible = self.opts.rh_surfs_on
except AttributeError:
pass
@on_trait_change('opts:conns_colors_on')
def chg_conns_colors(self):
try:
if self.opts.conns_colors_on:
self.dv_3d.vectors.glyph.color_mode = 'color_by_scalar'
else:
self.dv_3d.vectors.glyph.color_mode = 'no_coloring'
except AttributeError:
pass
@on_trait_change('opts:conns_colorbar')
def chg_conns_colorbar(self):
try:
self.dv_3d.set_colorbar(self.opts.conns_colorbar,
self.dv_3d.vectors,
orientation='horizontal')
except AttributeError:
pass
@on_trait_change('opts:conns_width')
def chg_conns_width(self):
try:
self.dv_3d.vectors.actor.property.line_width = self.opts.conns_width
except AttributeError:
pass
@on_trait_change('opts:default_map.[cmap,reverse,fname,threshold]')
def chg_default_map(self):
try:
self.draw_nodes()
except:
map_def = self.opts.default_map
if map_def.cmap == 'file' and not map_def.fname:
pass
else:
raise
@on_trait_change('opts:scalar_map.[cmap,reverse,fname,threshold]')
def chg_scalar_map(self):
try:
self.draw_surfs()
self.draw_nodes()
except:
map_sca = self.opts.scalar_map
if map_sca.cmap == 'file' and not map_sca.fname:
pass
else:
raise
@on_trait_change('opts:activation_map.[cmap,reverse,fname,threshold]')
def chg_activation_map(self):
#we don't touch the circle plot here since circle redraw is expensive
try:
self.dv_3d.draw_conns()
except:
map_act = self.opts.activation_map
if map_act.cmap == 'file' and not map_act.fname:
pass
else:
raise
@on_trait_change('opts:connmat_map.[cmap,reverse,fname,threshold]')
def chg_connmat_map(self):
try:
self.dv_mat.change_colormap()
except:
map_mat = self.opts.connmat_map
if map_mat.cmap == 'file' and not map_mat.fname:
pass
else:
raise
######################################################################
# MISCELLANEOUS HELPERS
######################################################################
def error_dialog(self, str):
return self.gui.error_dialog(str)
def warning_dialog(self, str):
return self.gui.warning_dialog(str)
def verbose_msg(self, str):
return self.gui.verbose_msg(str)
def get_module(self):
if self.cur_module == 'custom':
return self.custom_module
elif isinstance(self.cur_module, int) and self.modules is not None:
return self.modules[self.cur_module]