# Generate random data and random connectivity
data = np.random.uniform(low=-1., high=1., size=(n_sources, ))
conn = np.triu(np.random.uniform(-1., 1., (n_sources, n_sources)))
conn_select = (-.005 < conn) & (conn < .005)
# Scene creation
sc = SceneObj()
###############################################################################
# Animate a single brain object
###############################################################################
# Here we set an animation for a single brain object.
b_obj_1 = BrainObj('inflated', translucent=False)
b_obj_1.animate()
sc.add_to_subplot(b_obj_1, rotate='left', title='Animate a single object')
###############################################################################
# Animate multiple objects
###############################################################################
# Here we animate multiple objects inside a subplot
s_obj_1 = SourceObj('s1', xyz, data=data)
b_obj_2 = BrainObj('white')
b_obj_2.animate()
sc.add_to_subplot(s_obj_1, row=1, title='Animate multiple objects')
sc.add_to_subplot(b_obj_2, row=1, rotate='right', use_this_cam=True)
###############################################################################
def vis_sources_by_pval(padj, all_coord, short_label):
'''
1. visualize the sources (brain regions) based on the FDR-adjusted p values
display both the source objects and the brain object
'''
# Define the default camera state used for each subplot
CAM_STATE = dict(
azimuth=0, # azimuth angle
elevation=90, # elevation angle
scale_factor=180 # distance to the camera
)
S_KW = dict(camera_state=CAM_STATE)
# Create the scene
CBAR_STATE = dict(cbtxtsz=12,
txtsz=13.,
width=.5,
cbtxtsh=2.,
rect=(1., -2., 1., 4.))
sc = SceneObj(bgcolor='black', size=(1600, 1000))
# mask = padj<=0.05 # significant regions with true label
data0, max_p, min_p = get_log_pval(padj)
b_obj = BrainObj('white', hemisphere='both', translucent=True)
b_obj.animate(iterations=10, step=30, interval=1)
s_obj = SourceObj(
's1',
all_coord, #all_coord,
# mask = mask,
# mask_color='white',
# mask_radius = 15,
color=padj * 100,
data=data0,
text=short_label,
text_size=10,
text_bold=True,
text_color='yellow',
symbol='disc',
visible=True,
radius_min=10,
radius_max=40,
alpha=0.65)
s_obj_1.set_visible_sources('left')
s_obj.color_sources(data=data0, cmap='jet', clim=(min_p, max_p))
cb_data = ColorbarObj(s_obj,
cblabel='Log FDR-adjusted p value',
border=False,
**CBAR_STATE)
sc.add_to_subplot(b_obj, row=0, col=0, rotate='front')
sc.add_to_subplot(s_obj, row=0, col=0)
sc.add_to_subplot(cb_data, row=0, col=1, width_max=90)
# recording animation and save it
sc.record_animation('output/animate_pvalue_projection.gif', n_pic=10)
sc.preview()
# Generate random data and random connectivity
data = np.random.uniform(low=-1., high=1., size=(n_sources,))
conn = np.triu(np.random.uniform(-1., 1., (n_sources, n_sources)))
conn_select = (-.005 < conn) & (conn < .005)
# Scene creation
sc = SceneObj()
###############################################################################
# Animate a single brain object
###############################################################################
# Here we set an animation for a single brain object.
b_obj_1 = BrainObj('inflated', translucent=False)
b_obj_1.animate()
sc.add_to_subplot(b_obj_1, rotate='left', title='Animate a single object')
###############################################################################
# Animate multiple objects
###############################################################################
# Here we animate multiple objects inside a subplot
s_obj_1 = SourceObj('s1', xyz, data=data)
b_obj_2 = BrainObj('white')
b_obj_2.animate()
sc.add_to_subplot(s_obj_1, row=1, title='Animate multiple objects')
sc.add_to_subplot(b_obj_2, row=1, rotate='right', use_this_cam=True)
def vis_brainSurface_by_padj(padj_parcellates,
gci_parcellates,
tt,
ptype='significant',
cmap='jet',
use_log=False,
use_1_p=False,
use_p=True):
# '''visualize the brain surface based on the FDR-adjusted GCI values or FDR-adjusted p values'''
# Define the default camera state used for each subplot
CAM_STATE = dict(
azimuth=0, # azimuth angle
elevation=90, # elevation angle
scale_factor=180 # distance to the camera
)
S_KW = dict(camera_state=CAM_STATE)
# Create the scene
CBAR_STATE = dict(cbtxtsz=12,
txtsz=13.,
width=.5,
cbtxtsh=2.,
rect=(1., -2., 1., 4.))
sc = SceneObj(bgcolor='black', size=(1300, 1000))
# n_sources = all_coord.shape[0] #sig_roi_coord.shape[0]
# data = np.random.rand(n_sources)
# 1. significant regions
b_obj_1 = BrainObj('white', translucent=False)
# parcellize brain based on desikan atlas
path_to_file1 = download_file('lh.aparc.annot', astype='example_data')
path_to_file2 = download_file('rh.aparc.annot', astype='example_data')
# dataframe type varaible inlcuding index, label, color for each brain region in DKT atlas
lh_df = b_obj_1.get_parcellates(path_to_file1)
rh_df = b_obj_1.get_parcellates(path_to_file2)
# lh_df.to_excel('output/lh.aparc.xlsx')
def select_rois(df, row=1, col=1):
select_val = list(np.array(df.iloc[row:, col]))
select_val.pop(3)
return select_val
select_roi1 = select_rois(lh_df)
select_roi2 = select_rois(rh_df)
# get log FDR-adjusted p values (optional)
# log_p_parce, l_p, s_p = get_log_pval(padj_parcellates, basis=2)
l_p = np.max(gci_parcellates)
s_p = np.min(gci_parcellates)
# print('-----#####',l_p,s_p)
if ptype == 'significant' or ptype == 'unsignificant':
def get_hemisphere_rois(lh_padj,
lh_gci,
lh_rois,
select='significant',
cal_log=True):
if select == 'significant':
lh_sig_ind = np.where(np.array(lh_padj) <= 0.05)[0]
else:
lh_sig_ind = np.where(np.array(lh_padj) > 0.05)[0]
lh_sig_rois = [lh_rois[i] for i in lh_sig_ind]
if cal_log:
log_p, _, _ = get_log_pval(lh_padj,
basis=2) # calculate "log2(padj)"
lh_sig_padj = list(np.array(log_p)[lh_sig_ind])
else:
# lh_sig_padj = list(np.array(lh_padj)[lh_sig_ind])
lh_sig_gci = list(np.array(lh_gci)[lh_sig_ind])
# max_p= np.max(np.array(lh_sig_padj))
# min_p= np.min(np.array(lh_sig_padj))
# return lh_sig_rois,lh_sig_padj,max_p,min_p
max_gci = np.max(np.array(lh_sig_gci))
min_gci = np.min(np.array(lh_sig_gci))
return lh_sig_rois, lh_sig_gci, max_gci, min_gci
# (1). set (log-padj) as values for color mapping
# select_regions_L,lh_padj,_,_ = get_hemisphere_rois(padj_parcellates[:34], gci_parcellates[:34], select_roi1, select = ptype, cal_log=use_log)
# select_regions_R,rh_padj,_,_ = get_hemisphere_rois(padj_parcellates[34:], gci_parcellates[34:], select_roi2, select = ptype, cal_log=use_log)
# clab = 'Log FDR-adjusted p value'
# b_obj_1.parcellize(path_to_file1, select= select_regions_L,data=lh_padj,cmap=cmap,clim=[s_p,l_p])
# b_obj_1.parcellize(path_to_file2, select= select_regions_R,data=rh_padj,cmap=cmap,clim=[s_p,l_p])
# cb_1 = ColorbarObj(b_obj_1, clim= [s_p,l_p], cblabel=clab, border=False, **CBAR_STATE)
# plot GCI value-4/23/2020
select_regions_L, lh_gci, _, _ = get_hemisphere_rois(
padj_parcellates[:34],
gci_parcellates[:34],
select_roi1,
select=ptype,
cal_log=use_log)
select_regions_R, rh_gci, _, _ = get_hemisphere_rois(
padj_parcellates[34:],
gci_parcellates[34:],
select_roi2,
select=ptype,
cal_log=use_log)
clab = 'GCI value'
b_obj_1.parcellize(path_to_file1, select=select_regions_L,
data=lh_gci) #clim=[s_p,l_p]
b_obj_1.parcellize(path_to_file2,
select=select_regions_R,
data=rh_gci,
cmap=cmap) #, clim=[s_p,l_p])
cb_1 = ColorbarObj(b_obj_1,
clim=[1.76, 1.80],
cblabel=clab,
border=False,
**CBAR_STATE)
elif ptype == 'together':
select_regions_L = select_roi1
select_regions_R = select_roi2
if use_log:
# (1). set (log-padj) as values for color mapping
clab = 'Log FDR-adjusted p value'
lh_padj = log_p_parce[:34]
rh_padj = log_p_parce[34:]
b_obj_1.parcellize(path_to_file1,
select=select_regions_L,
data=lh_padj,
cmap=cmap,
clim=[s_p, l_p])
b_obj_1.parcellize(path_to_file2,
select=select_regions_R,
data=rh_padj,
cmap=cmap,
clim=[s_p, l_p])
cb_1 = ColorbarObj(b_obj_1,
clim=[s_p, l_p],
cblabel=clab,
border=False,
**CBAR_STATE)
if use_1_p:
# (2). set (1-padj) as values for color mapping
clab = tt #'1-FDR-adjusted p value'
# clab = '1-FDR-adjusted p value'
padj_0 = [1 - i for i in padj_parcellates]
b_obj_1.parcellize(path_to_file1,
select=select_regions_L,
data=padj_0[:34],
cmap=cmap)
b_obj_1.parcellize(path_to_file2,
select=select_regions_R,
data=padj_0[34:],
cmap=cmap)
cb_1 = ColorbarObj(b_obj_1,
cblabel=clab,
border=False,
**CBAR_STATE) #Log FDR-adjusted p value
if use_p:
# (2). set (1-padj) as values for color mapping
print('--------use p-------')
clab = tt #'1-FDR-adjusted p value'
mx = np.array(gci_parcellates).max()
mi = np.array(gci_parcellates).min()
b_obj_1.parcellize(path_to_file1,
select=select_regions_L,
data=gci_parcellates[:34],
cmap=cmap,
clim=[mi, mx])
b_obj_1.parcellize(path_to_file2,
select=select_regions_R,
data=gci_parcellates[34:],
cmap=cmap,
clim=[mi, mx])
cb_1 = ColorbarObj(b_obj_1,
cblabel=clab,
border=False,
**CBAR_STATE) #Log FDR-adjusted p value
b_obj_1.animate(iterations=10, step=30, interval=1.2)
sc.add_to_subplot(b_obj_1, row=0, col=0, rotate='front')
sc.add_to_subplot(cb_1, row=0, col=1, width_max=90)
# sc.record_animation('output/%s_pvalue_projection.gif'%ptype, n_pic=10)
# sc.record_animation('output/pmci_degree.gif', n_pic=8)
sc.preview()
