def plot_fmri_subject(lst):
sdir = ['_RL', '_LR']
scan_type = ['left', 'right']
session_type = [1, 2]
data_file = 'validation'
class sc:
pass
for sub in lst:
for hemi in range(0, 2):
left = np.load(data_file + str(sub) + '_' + scan_type[hemi] +
sdir[0] + '_' + str(session_type[0]) + '.npz')
sc.labels = left['labels']
sc.vertices = left['vertices']
sc.faces = left['faces']
sc.vColor = np.zeros([sc.vertices.shape[0]])
sc = patch_color_labels(sc, cmap='Paired', shuffle=True)
view_patch(sc, show=1, colormap='Paired', colorbar=0)
s = r
s.vColor = sp.zeros(s.vertices.shape)
label_vert, lab_count = sp.stats.mode(labs_all.T)
colr = get_cmap(nClusters + 1)
lab_count = sp.float32(lab_count.squeeze())
s.vColor = s.vColor + 1
for i in range(len(s.vertices)):
# print i, (lab_count[i]/sp.amax(lab_count)), colr(label_vert[0,i])[:3], (lab_count[i]/sp.amax(lab_count)), 0.55*sp.array(colr(label_vert[0,i])[:3])
if label_vert[0, i] > 0:
freq = ((lab_count[i] / sp.amax(lab_count)) -
1.0 / nClusters) * (sp.float32(nClusters) / (nClusters - 1.0))
s.vColor[i, ] = (1 -
freq) + freq * sp.array(colr(label_vert[0, i])[:3])
view_patch(s)
view_patch_vtk(s)
writedfs('outclustering_pc.dfs', s)
# view_patch(r,r.labels)
#mesh = mlab.triangular_mesh(r.vertices[:,0], r.vertices[:,1], r.vertices[:,2], r.faces, representation='surface',
# opacity=1,scalars=np.float64(r.labels))
# #mlab.pipeline.surface(mesh)
#mlab.gcf().scene.parallel_projection = True
#mlab.view(azimuth=0, elevation=-90)
#mlab.show()
#mlab.savefig(filename = 'dict_learning_1.png')
#mlab.view(azimuth=0, elevation=90)
#mlab.savefig(filename = 'dict_learning_2.png')
#mlab.close()
s2 = sp.load('labs_all_split2_data1.npz')
l = s2['lab_sub2']
l2 = sp.reshape(l, (l.shape[0] * l.shape[1]), order='F')
l12 = sp.concatenate((l1[:, None], l2[:, None]), axis=1)
print sp.sum(sp.absolute(l12[:, 1] - l12[:, 0]))
l12 = reorder_labels(l12)
print sp.sum(sp.absolute(l12[:, 1] - l12[:, 0]))
l1 = sp.reshape(l12[:, 0], (l.shape[0], l.shape[1]), order='F')
l2 = sp.reshape(l12[:, 1], (l.shape[0], l.shape[1]), order='F')
#
for ind in range(l.shape[1]):
lab1 = l1[:, ind]
view_patch(dfs_left_sm,
lab1,
show=0,
outfile=lst[ind] + 'labs_all_split1_2_data1.png')
view_patch(dfs_left_sm,
lab1,
show=0,
outfile=lst[ind + l.shape[1]] + 'labs_all_split2_2_data1.png')
# lab1=l2[:,ind]
# view_patch(dfs_left_sm,lab1,show=0,outfile=lst[ind]+'_individual_rot_data2.png')
#
arr1_view = arr1.view([('',arr1.dtype)]*arr1.shape[1])
arr2_view = arr2.view([('',arr2.dtype)]*arr2.shape[1])
intersected = sp.intersect1d(arr1_view, arr2_view)
return intersected.view(arr1.dtype).reshape(-1, arr1.shape[1])
inputfile='/home/ajoshi/data/Yeo_JNeurophysiol11_FreeSurfer/fsaverage/label/lh.Yeo2011_17Networks_N1000.annot'
fsavesurf='/home/ajoshi/data/Yeo_JNeurophysiol11_FreeSurfer/fsaverage/surf/lh.sphere.reg.avg'
yeomap,_,_=fsio.read_annot(inputfile)
vert,faces=fsaverage_surf=fsio.read_geometry(fsavesurf)
class s:
pass
s.vertices=vert; s.faces=faces;s.labels=yeomap
view_patch(s,yeomap)
#g_surf = gread('/home/ajoshi/data/HCP_data/reference/100307/MNINonLinear/fsaverage_LR32k/100307.L.sphere.32k_fs_LR.surf.gii')
g_surf = gread('/home/ajoshi/data/HCP_data/reference/100307/MNINonLinear/Native/100307.L.sphere.reg.native.surf.gii')
vert = g_surf.darrays[0].data
face = g_surf.darrays[1].data
class hcp32k:
pass
hcp32k.vertices=vert; hcp32k.faces=face
view_patch(hcp32k,vert[:,1])
from scipy.spatial import cKDTree
# brain=smooth_patch(brain,10)
# skull = readdfs(skull_name)
# skull = reducepatch(skull,.9,0)
brain = add_normals(brain)
# brain.normals=brain.vertices/5.0
skull = add_normals(skull)
# view_patch(brain)
# view_patch(skull)
writedfs('brain.dfs', brain)
writedfs('skull.dfs', skull)
print "# vertices in Brain: " + str(brain.vertices.shape[0])
print "# vertices in Skull: " + str(skull.vertices.shape[0])
m = mean_curvature(brain)
# m = smooth_surf_function(brain, m, 10, 100)
br = view_patch(brain, attrib=m, opacity=1, show=0)
view_patch(skull, opacity=.1, fig=br, show=1)
Tri = face_v_conn(brain)
Q = 1.0 + sp.zeros((brain.faces.shape[0], 1))
Q = (1.0 / 3.0) * Tri * Q
Q = Q[:, 0]
br_face_area = face_areas(brain)
Q = 1.0 + sp.zeros(brain.vertices.shape[0])
# Q=0.0*Q
# Q[1800]=1
# Q=smooth_surf_function(brain,Q,10,10)
area_v = (1.0 / 3.0) * Tri * br_face_area
v = sp.zeros(skull.vertices.shape[0]) # eq 8 from Sarvas
v_aaj = sp.zeros(skull.vertices.shape[0]) # joshi
view_patch(brain, attrib=Q, opacity=1)
for ind in range(len(roilist)):
msk_roi = np.in1d(dfs_left.labels, roilist[ind])
mlab.points3d(sub2_rot[msk_roi, 0],
sub2_rot[msk_roi, 1],
sub2_rot[msk_roi, 2],
scale_factor=0.05,
color=tuple(clr[ind]))
#
# mlab.points3d(sub2[:,0], sub2[:,1], sub2[:,2], scale_factor=0.05, color=(0, 1, 0))
mlab.draw()
mlab.savefig('sph3.png')
mlab.show()
dfs_left_sm.vColor = sp.zeros(dfs_left_sm.vertices.shape) + 0.5
view_patch(dfs_left_sm, close=0)
sub_vert = dfs_left_sm.vertices
for ind in range(len(roilist)):
msk_roi = np.in1d(dfs_left.labels, roilist[ind])
mlab.points3d(sub_vert[msk_roi, 0],
sub_vert[msk_roi, 1],
sub_vert[msk_roi, 2],
scale_factor=5,
color=tuple(clr[ind]))
#
#
## msk_small_region = (dfs_left.labels == 30) | (dfs_left.labels == 72) | (dfs_left.labels == 9) | (dfs_left.labels == 47) # % motor
#d = sub1 # [msk_small_region, :]
#
#ref_mean_pc = sp.mean(d,axis=0)
sub_data[:,:,count1] = d
count1+=1
print count1,
nSub=sub_data.shape[2]
cat_data=sp.zeros((nSub*sub_data.shape[0],sub_data.shape[1]))
for ind in range(nSub):
sub_data[:,:,ind] = rot_sub_data(ref=sub_data[:,:,0],sub=sub_data[:,:,ind])
cat_data[sub_data.shape[0]*ind:sub_data.shape[0]*(ind+1),:] = sub_data[:,:,ind]
print ind, sub_data.shape, cat_data.shape
rho=sp.corrcoef(cat_data)
rho[~np.isfinite(rho)] = 1
dist_mtx=abs(sp.arccos(rho))
SC = AgglomerativeClustering(n_clusters=nClusters, affinity='precomputed', linkage='complete')
#SC = DBSCAN(eps=sp.pi/102.0, metric='precomputed')
labs_all = SC.fit_predict(dist_mtx)+1
lab_sub=labs_all.reshape((sub_data.shape[0],nSub),order='F')
lab1=sp.zeros(dfs_left_sm.vertices.shape[0])
for ind in range(nSub):
# lab1[msk_small_region]=lab_sub[:,ind]
lab1=lab_sub[:,ind]
view_patch(dfs_left_sm,lab1)
l1 = sp.reshape(l12[:, 0], (l.shape[0], l.shape[1] / 2.0), order='F')
l2 = sp.reshape(l12[:, 1], (l.shape[0], l.shape[1] / 2.0), order='F')
perm1 = sp.mod(17 * sp.arange(max(l1.flatten()) + 1), max(l1.flatten()) + 1)
#
for ind in range(5): #range(l1.shape[1]):
lab1 = sp.zeros(dfs_left_sm.vertices.shape[0], dtype=sp.int16)
lab2 = lab1.copy()
lab1[msk_small_region] = l1[:, ind]
counts1 = itemfreq(lab1)
lab2[msk_small_region] = l2[:, ind]
counts2 = itemfreq(lab2)
view_patch(dfs_left_sm,
perm1[lab1],
colorbar=0,
show=0,
elevation=-90,
colormap='Paired',
outfile=lst[ind] +
'_joint_both_session1_view1_8_clusters_precuneus.png')
view_patch(dfs_left_sm,
perm1[lab1],
colorbar=0,
show=0,
elevation=90,
colormap='Paired',
outfile=lst[ind] +
'_joint_both_session1_view2_8_clusters_precuneus.png')
view_patch(dfs_left_sm,
perm1[lab2],
colorbar=0,
show=0,
sub_data[:, :, count1] = d
count1 += 1
print count1,
nSub = sub_data.shape[2]
cat_data = sp.zeros((nSub * sub_data.shape[0], sub_data.shape[1]))
for ind in range(nSub):
sub_data[:, :, ind] = rot_sub_data(ref=sub_data[:, :, 0],
sub=sub_data[:, :, ind])
cat_data[sub_data.shape[0] * ind:sub_data.shape[0] *
(ind + 1), :] = sub_data[:, :, ind]
print ind, sub_data.shape, cat_data.shape
rho = sp.corrcoef(cat_data)
rho[~np.isfinite(rho)] = 1
simil_mtx = sp.pi / 2.0 + sp.arcsin(rho)
SC = SpectralClustering(n_clusters=nClusters, affinity='precomputed')
labs_all = SC.fit_predict(simil_mtx) + 1
lab_sub = labs_all.reshape((sub_data.shape[0], nSub), order='F')
lab1 = sp.zeros(dfs_left_sm.vertices.shape[0])
for ind in range(nSub):
# lab1[msk_small_region]=lab_sub[:,ind]
lab1 = lab_sub[:, ind]
view_patch(dfs_left_sm, lab1, outfile='')
l2 = sp.reshape(l[:, 40:80], (l.shape[0]*40), order='F')
l12 = sp.concatenate((l1[:, None], l2[:, None]), axis=1)
print sp.sum(sp.absolute(l12[:, 1]-l12[:, 0]))
print sp.sum(sp.absolute(l12[:, 1]-l12[:, 0]))
l1 = sp.reshape(l12[:, 0], (l.shape[0], 40), order='F')
l2 = sp.reshape(l12[:, 1], (l.shape[0], 40), order='F')
#
for ind in range(l12.shape[1]):
lab1 = sp.int32(l12[:, ind])
dfs_left_sm.labels = lab1
s = silhouette_samples(cat_data[nVert*(ind):nVert*(ind+1), :],
dfs_left_sm.labels)
s[s < 0] = 0
s = s/sp.median(s)
s[s > 1.0] = 1.0
s1 = patch_color_labels(dfs_left_sm, freq=s)
# view_patch(s1)
view_patch(s1, elevation=90, colorbar=0, show=0, outfile=lst[ind]+'_\
individual_view1_nclusters30_sil_modulated.png', colormap='Paired')
view_patch(s1, elevation=-90, colorbar=0, show=0, outfile=lst[ind]+'_\
individual_view2_nclusters30_sil_modulated.png', colormap='Paired')
LR_flag = msk['LR_flag'] LR_flag = np.squeeze(LR_flag) > 0 data = data['ftdata_NLM'] #data = sp.squeeze(vrest.get_data()).T vrest = data[LR_flag,:] m = np.mean(vrest, 1) vrest = vrest - m[:,None] s = np.std(vrest, 1)+1e-16 vrest2 = vrest/s[:,None] vrest2=rot_sub_data(ref=vrest1,sub=vrest2) rho_rot = sp.dot(vrest1,vrest2.T)/vrest1.shape[1] dist_mat = sp.absolute(sp.arccos(rho_rot)) ind = sp.argmin(dist_mat,axis=0) #ind = linear_assignment(dist_mat) view_patch(dfs_left_sm,outfile='before_registered_surf.png',show=1) dfs_left_sm.faces=ind[dfs_left_sm.faces] view_patch(dfs_left_sm,outfile='registered_surf1.png',show=1) #rho1=smooth_surf_function(dfs_left_sm,rho1) #rho2=smooth_surf_function(dfs_left_sm,rho2) #view_patch(dfs_left_sm,rho_orig,clim=[0,1]) #view_patch(dfs_left_sm,rho_rot,clim=[0,1],show=1)
labels = np.squeeze(labs.get_data())
g_surf = nib.load('/home/ajoshi/data/HCP_data/reference/100307/MNINonL\
inear/fsaverage_LR32k/100307.L.very_inflated.32k_fs_LR.surf.gii')
s.vertices = g_surf.darrays[0].data
s.faces = g_surf.darrays[1].data
s.labels = np.zeros(s.vertices.shape[0])
s.labels[idxs] = labels
'''h32k to full res FS'''
g_surf = nib.load('/home/ajoshi/data/HCP_data/reference/100307/MNINonLinear/N\
ative/100307.L.very_inflated.native.surf.gii')
h.vertices = g_surf.darrays[0].data
h.faces = g_surf.darrays[1].data
h = interpolate_labels(s, h)
h = patch_color_labels(h)
view_patch(h)
''' native FS ref to native FS BCI'''
g_surf = nib.load('/home/ajoshi/data/HCP_data/reference/100307/MNINon\
Linear/Native/100307.L.sphere.reg.native.surf.gii')
s.vertices = g_surf.darrays[0].data
s.faces = g_surf.darrays[1].data
s.labels = h.labels
''' map to bc sphere'''
bs.vertices, bs.faces = fsio.read_geometry('/home/ajoshi/data/BCI_DNI_Atlas/su\
rf/lh.sphere.reg')
bs = interpolate_labels(s, bs)
bci.vertices, bci.faces = fsio.read_geometry('/home/ajoshi/data/BCI_DNI_A\
tlas/surf/lh.white')
bci.labels = bs.labels
writedfs('BCI_orig_lh.dfs', bci)
rho1 += sp.sum(vrest1 * vlang1, axis=1) / vrest1.shape[1]
rho2 += sp.sum(vrest2 * vlang2, axis=1) / vlang1.shape[1]
vlang1, _ = rot_sub_data(ref=vrest1, sub=vlang1)
vlang2, _ = rot_sub_data(ref=vrest2, sub=vlang2)
rho1rot += sp.sum(vrest1 * vlang1, axis=1) / vrest1.shape[1]
rho2rot += sp.sum(vrest2 * vlang2, axis=1) / vlang1.shape[1]
rho1 = smooth_surf_function(dfs_left_sm, rho1, a1=0, a2=1)
rho1rot = smooth_surf_function(dfs_left_sm, rho1rot, a1=0, a2=1)
view_patch(dfs_left_sm,
rho1 / len(lst),
clim=[0, 0.75],
outfile='rest1lang_before_rot.png',
show=0)
view_patch(dfs_left_sm,
rho1rot / len(lst),
clim=[0, 0.75],
outfile='rest1lang_after_rot.png',
show=0)
#rho2 = smooth_surf_function(dfs_left_sm, rho2, a1=0, a2=1)
#rho2rot = smooth_surf_function(dfs_left_sm, rho2rot)
view_patch(dfs_left_sm,
rho2 / len(lst),
clim=[0, 0.75],
outfile='rest2lang_before_rot.png',
bci_bst.vertices[:, 1] -= 192 * 0.546875
bci_bst.vertices[:, 2] -= 192 * 0.546875
bci.vertices, bci.faces = fsio.read_geometry(
'/home/ajoshi/data/BCI_DNI_Atlas/surf/rh.white')
bci = interpolate_labels(bci_bst, bci)
''' FS_BCI to FS BCI Sphere'''
bci.vertices, bci.faces = fsio.read_geometry(
'/home/ajoshi/data/BCI_DNI_Atlas/surf/rh.sphere.reg')
''' FS BCI Sphere to ref FS Sphere'''
g_surf = gread(
'/big_disk/ajoshi/HCP_data/reference/100307/MNINonLinear/Native/100307.R.sphere.reg.native.surf.gii'
)
s.vertices = g_surf.darrays[0].data
s.faces = g_surf.darrays[1].data
s = interpolate_labels(bci, s)
''' ref BCI Sphere to FS very inflated '''
g_surf = gread(
'/big_disk/ajoshi/HCP_data/reference/100307/MNINonLinear/Native/100307.R.very_inflated.native.surf.gii'
)
bci.vertices = g_surf.darrays[0].data
bci.faces = g_surf.darrays[1].data
bci.labels = s.labels
''' FS very inflated to reduce3 '''
dfs = readdfs(
'/big_disk/ajoshi/HCP_data/reference/100307.aparc.a2009s.32k_fs.reduce3.very_smooth.right.dfs'
)
dfs = interpolate_labels(bci, dfs, skipzero=1)
view_patch(dfs, dfs.labels, colormap='prism')
writedfs('100307.reduce3.very_smooth.right.refined.dfs', dfs)
rho1 += sp.sum(vrest1 * vrest2, axis=1) / vrest1.shape[1]
rho1lang += sp.sum(vlang1 * vlang2, axis=1) / vlang1.shape[1]
vrest2, _ = rot_sub_data(ref=vrest1, sub=vrest2)
vlang2, _ = rot_sub_data(ref=vlang1, sub=vlang2)
rho2 += sp.sum(vrest2 * vrest2, axis=1) / vrest1.shape[1]
rho2lang += sp.sum(vlang1 * vlang2, axis=1) / vlang1.shape[1]
rho1 = smooth_surf_function(dfs_left_sm, rho1, a1=0, a2=1)
rho2 = smooth_surf_function(dfs_left_sm, rho2, a1=0, a2=1)
view_patch(dfs_left_sm,
rho1 / len(lst),
clim=[0, 1],
outfile='rest_before_rot.png',
show=0)
view_patch(dfs_left_sm,
rho2 / len(lst),
clim=[0, 1],
outfile='rest_after_rot.png',
show=0)
rho1lang = smooth_surf_function(dfs_left_sm, rho1lang, a1=0, a2=1)
rho2lang = smooth_surf_function(dfs_left_sm, rho2lang, a1=0, a2=1)
view_patch(dfs_left_sm,
rho1lang / len(lst),
clim=[0, 1],
outfile='lang_before_rot.png',
lab2 = l2[:, ind2]
nVert = dfs_left_sm.vertices.shape[0]
cat_data1 = s1['cat_data1']
dfs_left_sm.labels = lab1
s = silhouette_samples(cat_data1[nVert * (ind):nVert * (ind + 1), :],
dfs_left_sm.labels)
s[s < 0] = 0
s = s / sp.median(s)
s[s > 1.0] = 1.0
dfs_left_sm = patch_color_labels(dfs_left_sm, freq=s)
view_patch(dfs_left_sm,
colorbar=0,
colormap='Paired',
elevation=90,
show=0,
outfile=lst[ind] + '_view1_split1_30clusters_1common_modulated.png')
view_patch(dfs_left_sm,
colorbar=0,
colormap='Paired',
elevation=-90,
show=0,
outfile=lst[ind] + '_view2_split1_30clusters_1common_modulated.png')
cat_data2 = s1['cat_data2']
dfs_left_sm.labels = lab2
s = silhouette_samples(cat_data2[nVert * (ind2):nVert * (ind2 + 1), :],
dfs_left_sm.labels)
s[s < 0] = 0
s = s / sp.median(s)
pass
class r:
pass
''' HCP32k data'''
labs = nib.load('/home/ajoshi/data/Glasser_et_al_2016_HCP_MMP1.0_RVVG/HCP_PhaseTwo/Q1-Q6_RelatedParcellation210/MNINonLinear/fsaverage_LR32k/Q1-Q6_RelatedParcellation210.R.CorticalAreas_dil_Colors.32k_fs_LR.dlabel.nii')
cifti = etree.XML(labs.header.extensions[0].get_content())
idxs=np.array(cifti[0][2][0][0].text.split(' ')).astype(np.int)
labels = sp.squeeze(labs.get_data())
g_surf = nib.load('/home/ajoshi/data/Glasser_et_al_2016_HCP_MMP1.0_RVVG/HCP_PhaseTwo/Q1-Q6_RelatedParcellation210/MNINonLinear/fsaverage_LR32k/Q1-Q6_RelatedParcellation210.R.inflated_MSMAll_2_d41_WRN_DeDrift.32k_fs_LR.surf.gii')
s.vertices = g_surf.darrays[0].data; s.faces = g_surf.darrays[1].data
s.labels=sp.zeros(s.vertices.shape[0])
s.labels[idxs] = labels
view_patch(s, s.labels,colormap='Paired')
''' ref BCI Sphere to FS very inflated '''
g_surf = nib.load('/home/ajoshi/data/HCP_data/reference/100307/MNINonLinear/fsaverage_LR32k/100307.R.very_inflated.32k_fs_LR.surf.gii')
r.vertices = g_surf.darrays[0].data; r.faces = g_surf.darrays[1].data
r.labels=s.labels
''' FS very inflated to reduce3 '''
dfs = readdfs('/home/ajoshi/data/HCP_data/reference/100307.aparc.a2009s.32k_fs.reduce3.very_smooth.right.dfs')
dfs = interpolate_labels(r,dfs, skipzero=1)
view_patch(dfs,dfs.labels, colormap='Paired')
writedfs('100307.reduce3.Glasser.right.dfs', dfs)
s.faces = g_surf.darrays[1].data
s.labels = h.labels
''' map to bc sphere'''
bs.vertices, bs.faces = fsio.read_geometry('/home/ajoshi/data/BCI_DNI_Atlas/su\
rf/rh.sphere.reg')
bs = interpolate_labels(s, bs)
bci.vertices, bci.faces = fsio.read_geometry('/home/ajoshi/data/BCI_DNI_A\
tlas/surf/rh.white')
bci.labels = bs.labels
writedfs('BCI_orig_rh.dfs', bci)
bci.vertices, bci.faces = fsio.read_geometry('/home/ajoshi/data/BCI_DNI_A\
tlas/surf/rh.inflated')
view_patch(bci, bci.labels)
writedfs('BCI_pial_rh.dfs.', bci)
bci.vertices, bci.faces = fsio.read_geometry('/home/ajoshi/data/BCI_DNI_Atla\
s/surf/rh.white')
writedfs('BCI_white_rh.dfs.', bci)
bci.vertices[:, 0] += 96*0.8
bci.vertices[:, 1] += 192*0.546875
bci.vertices[:, 2] += 192*0.546875
bci_bst = readdfs('/home/ajoshi/data/BCI-DNI_brain_atlas/BCI-DNI_brain.right.\
inner.cortex.dfs')
bci_bst = interpolate_labels(bci, bci_bst)
print sp.sum(sp.absolute(l12[:, 1] - l12[:, 0]))
l1 = sp.reshape(l12[:, 0], (l.shape[0], l.shape[1]), order='F')
l2 = sp.reshape(l12[:, 1], (l.shape[0], l.shape[1]), order='F')
perm1 = sp.mod(17 * sp.arange(max(l1.flatten()) + 1), max(l1.flatten()) + 1)
# Plot labels
for ind in range(4): #range(l.shape[1]):
lab1 = l1[:, ind]
lab2 = l2[:, ind]
view_patch(dfs_left_sm,
perm1[lab1],
colorbar=0,
colormap='Paired',
elevation=90,
show=0,
outfile=lst[ind] + '_view1_split1_60clusters.png')
view_patch(dfs_left_sm,
perm1[lab1],
colorbar=0,
colormap='Paired',
elevation=-90,
show=0,
outfile=lst[ind] + '_view2_split1_60clusters.png')
view_patch(dfs_left_sm,
perm1[lab2],
colorbar=0,
colormap='Paired',
elevation=90,
