def map_pits_heritability(template, side, areals, indir, cb, interval_cb,
dict_freq, pval_thresh, outdir):
"""
Parameters
template: path to template mesh file
areals: path to gii file containing the texture of areals
side: hemisphere side from which SOLAR estimates are displayed
indir: directory containing h2 and pval dictionaries
cb: colorbar names must be an array with [cb_h2, cb_pval]
interval_cb: number boundaries for the colobar display
must be a double array [inter_h2[0,1], inter_pval[0,1]]
dict_freq: dictionary containing pits frequency per areal
pval_thresh: p-value threshold
outdir: output directory for the snapshots
"""
if not os.path.isdir(outdir):
os.makedirs(outdir)
# Define the quaternions that are gonna be used for the snapshots
if (side == "L"):
view_quaternions = {
'intern': [0.5, 0.5, 0.5, 0.5],
'extern': [0.5, -0.5, -0.5, 0.5],
'bottom': [1, 0, 0, 0],
'top': [0, 0, 1, 0]
}
else:
view_quaternions = {
'intern': [0.5, 0.5, 0.5, 0.5],
'extern': [0.5, -0.5, -0.5, 0.5],
'bottom': [1, 0, 0, 0],
'top': [0, 0, 1, 0]
}
# Load h2 and pval dictionaries
path_h2 = os.path.join(indir, 'h2_dict.json')
with open(path_h2, 'r') as f:
data = json.load(f)
dict_h2 = json.loads(data)
path_pval = os.path.join(indir, 'pval_dict.json')
with open(path_pval, 'r') as f:
data = json.load(f)
dict_pval = json.loads(data)
# Areal value in each vertex
array_areals = gio.read(areals).darrays[0].data
# Create arrays that will contain h2 and pval estimates on each vertex
array_h2 = np.zeros(len(array_areals))
array_pval = np.zeros(len(array_areals))
list_areals = np.unique(array_areals)
# Load the mesh in anatomist
mesh = ana.loadObject(template)
for areal in list_areals:
# make sure areal has the right format
areal = str(int(areal))
if (dict_h2[side].has_key(areal) and
# Check if areal passed the threshold frequency in both hemispheres
dict_freq['L']["Areal_" + areal] *
dict_freq['R']["Areal_" + areal] > 0):
# Finally, checl if the p-value pass the pval threshold
if dict_pval[side][areal] < pval_thresh:
# Select the index of vertices belonging to the areal
ind = np.where(array_areals == float(areal))[0]
# Give them the h2 estimate of this areal
array_h2[ind] = dict_h2[side][areal]
# And the log10 pvalue
array_pval[ind] = -np.log10(dict_pval[side][areal])
### Block performing the display of h2 estimates ###
# Create anatomist window
window = ana.createWindow('3D', geometry=[0, 0, 584, 584])
tex = aims.TimeTexture(dtype='FLOAT')
tex[0].assign(array_h2)
pits_tex = ana.toAObject(tex)
tex_mesh = ana.fusionObjects([mesh, pits_tex],
method='FusionTexSurfMethod')
tex_mesh.assignReferential(ref)
tex_mesh.setMaterial(front_face='counterclockwise')
pits_tex.setPalette(cb[0],
minVal=interval_cb[0][0],
maxVal=interval_cb[0][1],
absoluteMode=True)
updateWindow(window, tex_mesh)
# Loop through the quaternions and do the snapshot
for sd in view_quaternions.keys():
q = aims.Quaternion(view_quaternions[sd])
window.camera(view_quaternion=view_quaternions[sd], zoom=0.65)
# Snapshot file output
output = os.path.join(outdir,
'snapshot_h2_' + side + '_' + sd + '.png')
ana.execute('WindowConfig', windows=[window], snapshot=output)
#### Block performing the display of pvalues estimates ###
# Create anatomist window, second time (else zoom problem for snapshots)
window = ana.createWindow('3D', geometry=[0, 0, 584, 584])
tex = aims.TimeTexture(dtype='FLOAT')
tex[0].assign(array_pval)
pits_tex = ana.toAObject(tex)
tex_mesh = ana.fusionObjects([mesh, pits_tex],
method='FusionTexSurfMethod')
tex_mesh.assignReferential(ref)
tex_mesh.setMaterial(front_face='counterclockwise')
pits_tex.setPalette(cb[1],
minVal=interval_cb[1][0],
maxVal=interval_cb[1][1],
absoluteMode=True)
updateWindow(window, tex_mesh)
# Loop through the quaternions and do the snapshot
for sd in view_quaternions.keys():
q = aims.Quaternion(view_quaternions[sd])
window.camera(view_quaternion=view_quaternions[sd], zoom=0.65)
# Snapshot file output
output = os.path.join(outdir,
'snapshot_pval_' + side + '_' + sd + '.png')
ana.execute('WindowConfig', windows=[window], snapshot=output)
cursor_visibility=0)
if (side == "R" and "Freesurfer" not in path_parcels and not SYMMETRIC
) or (side == "L" and "Freesurfer" in path_parcels
and not SYMMETRIC) or (SYMMETRIC
and "Freesurfer" in path_parcels):
view_quaternions = {
'intern': [0.5, 0.5, 0.5, 0.5],
'extern': [0.5, -0.5, -0.5, 0.5]
}
else:
view_quaternions = {
'extern': [0.5, 0.5, 0.5, 0.5],
'intern': [0.5, -0.5, -0.5, 0.5]
}
for sd in view_quaternions.keys():
q = aims.Quaternion(view_quaternions[sd])
windows[side].camera(view_quaternion=view_quaternions[sd],
zoom=0.65)
ana.execute('WindowConfig',
windows=[windows[side]],
snapshot=OUTPUT + 'snapshot_' + pheno + '_' + sd +
'.jpg')
for side in sides:
if SYMMETRIC:
sid = 'L'
else:
sid = side
pheno = pheno0 + sds[side]
if "Freesurfer" in path_parcels:
def map_shared_genetic(template, sd, areals, indir, cb, interval_cb, rho,
trait, pval_thresh, outdir):
"""
Parameters
template: path to template mesh file
areals: path to gii file containing the texture of areals
sd: hemisphere side from which SOLAR estimates are displayed
indir: directory containing h2 and pval dictionaries
cb: colorbar names must be an array with [cb_h2, cb_pval]
interval_cb: number boundaries for the colobar display
must be a double array [inter_h2[0,1], inter_pval[0,1]]
pval_thresh: p-value threshold
outdir: output directory for the snapshots
"""
if not os.path.isdir(outdir):
os.makedirs(outdir)
# Define the quaternions that are gonna be used for the snapshots
if (sd == "L"):
view_quaternions = {
'intern': [0.5, 0.5, 0.5, 0.5],
'extern': [0.5, -0.5, -0.5, 0.5],
'bottom': [1, 0, 0, 0],
'top': [0, 0, 1, 0]
}
else:
view_quaternions = {
'intern': [0.5, -0.5, -0.5, 0.5],
'extern': [0.5, 0.5, 0.5, 0.5],
'bottom': [1, 0, 0, 0],
'top': [0, 0, 1, 0]
}
# Load h2 and pval dictionaries
path_h2 = os.path.join(indir, sd + rho + '_dict.json')
with open(path_h2, 'r') as f:
data = json.load(f)
dict_h2 = json.loads(data)
path_pval = os.path.join(indir, sd + rho + '_pval_dict.json')
with open(path_pval, 'r') as f:
data = json.load(f)
dict_pval = json.loads(data)
# Load h2 and pval dictionaries
rho2 = 'scipy_rhop'
path_h2 = os.path.join(indir, sd + rho2 + '_dict.json')
with open(path_h2, 'r') as f:
data = json.load(f)
dict_rhop = json.loads(data)
path_pval = os.path.join(indir, sd + rho2 + '_pval_dict.json')
with open(path_pval, 'r') as f:
data = json.load(f)
dict_pval_rhop = json.loads(data)
# Areal value in each vertex
array_areals = gio.read(areals).darrays[0].data
# Create arrays that will contain h2 and pval estimates on each vertex
array_h2 = np.zeros(len(array_areals))
array_pval = np.zeros(len(array_areals))
list_areals = np.unique(array_areals)
# Load the mesh in anatomist
mesh = ana.loadObject(template)
for areal in list_areals:
# make sure areal has the right format
areal = str(int(areal))
if dict_h2.has_key(areal + '_' + col):
# Finally, checl if the p-value pass the pval threshold
if (dict_pval[areal + '_' + col] < pval_thresh and
# Force the phenotypic correlation to be significant
dict_pval_rhop[areal + '_' + col] < 5e-2 / (2 * 180)):
# Select the index of vertices belonging to the areal
ind = np.where(array_areals == float(areal))[0]
# Give them the h2 estimate of this areal
if 'rhog' in rho: # Change this line for Figure 3
array_h2[ind] = abs(dict_h2[areal + '_' + col])
else:
array_h2[ind] = dict_h2[areal + '_' + col]
# And the log10 pvalue
array_pval[ind] = -np.log10(dict_pval[areal + '_' + col])
### Block performing the display of h2 estimates ###
# Create anatomist window
window = ana.createWindow('3D', geometry=[0, 0, 584, 584])
tex = aims.TimeTexture(dtype='FLOAT')
tex[0].assign(array_h2)
pits_tex = ana.toAObject(tex)
tex_mesh = ana.fusionObjects([mesh, pits_tex],
method='FusionTexSurfMethod')
tex_mesh.assignReferential(ref)
tex_mesh.setMaterial(front_face='counterclockwise')
pits_tex.setPalette(cb[0],
minVal=interval_cb[0][0],
maxVal=interval_cb[0][1],
absoluteMode=True)
updateWindow(window, tex_mesh)
ana.execute('TexturingParams', objects=[tex_mesh], interpolation='rgb')
# Loop through the quaternions and do the snapshot
for vw in view_quaternions.keys():
q = aims.Quaternion(view_quaternions[vw])
window.camera(view_quaternion=view_quaternions[vw], zoom=0.65)
# Snapshot file output
output = os.path.join(
outdir, '_'.join(['snapshot', trait, rho, sd, vw + '.png']))
ana.execute('WindowConfig', windows=[window], snapshot=output)
if False:
ana.releaseObject(pits_tex)
pits_tex = None
ana.releaseObject(tex_mesh)
tex_mesh = None
#### Block performing the display of pvalues estimates ###
# Create anatomist window, second time (else zoom problem for snapshots)
window = ana.createWindow('3D', geometry=[0, 0, 584, 584])
tex = aims.TimeTexture(dtype='FLOAT')
tex[0].assign(array_pval)
pits_tex = ana.toAObject(tex)
tex_mesh = ana.fusionObjects([mesh, pits_tex],
method='FusionTexSurfMethod')
tex_mesh.assignReferential(ref)
tex_mesh.setMaterial(front_face='counterclockwise')
pits_tex.setPalette(cb[1],
minVal=interval_cb[1][0],
maxVal=interval_cb[1][1],
absoluteMode=True)
updateWindow(window, tex_mesh)
ana.execute('TexturingParams', objects=[tex_mesh], interpolation='rgb')
# Loop through the quaternions and do the snapshot
for vw in view_quaternions.keys():
q = aims.Quaternion(view_quaternions[vw])
window.camera(view_quaternion=view_quaternions[vw], zoom=0.65)
# Snapshot file output
output = os.path.join(
outdir, '_'.join(['snapshot', trait, rho, 'pval', sd,
vw + '.png']))
ana.execute('WindowConfig', windows=[window], snapshot=output)
if False:
ana.releaseObject(pits_tex)
pits_tex = None
ana.releaseObject(tex_mesh)
tex_mesh = None