path_to_file2 = download_file(file2, astype='example_data')
# Define a brain object :
b_obj = BrainObj('inflated', hemisphere='both', translucent=False,
cblabel='Parcellates example', cbtxtsz=4.)
"""Parcellize the left hemisphere using the Destrieux Atlas. By default, no
parcellates are selected
"""
b_obj.parcellize(path_to_file1, hemisphere='left')
"""If you want to get the list of all predefined parcellates, use the
`get_parcellates` method which returns a pandas DataFrame with the index, the
name and the color associated to each parcellates
"""
df = b_obj.get_parcellates(path_to_file2)
# print(df)
"""Select only some parcellates. Note that this parcellization is using an
other atlas (Desikan-Killiany atlas)
"""
select = ['insula', 'paracentral', 'precentral', 'precuneus', 'frontalpole',
'temporalpole', 'fusiform', 'cuneus', 'inferiorparietal',
'inferiortemporal', 'precentral', 'superiorfrontal',
'superiortemporal']
"""Instead of using predefined colors inside the annot file, we use some data
"""
data = np.arange(len(select))
b_obj.parcellize(path_to_file2, hemisphere='right', select=select, data=data,
cmap='Spectral_r')
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()
# Define a brain object :
b_obj = BrainObj('inflated',
hemisphere='both',
translucent=False,
cblabel='Parcellates example',
cbtxtsz=4.)
"""Parcellize the left hemisphere using the Destrieux Atlas. By default, no
parcellates are selected
"""
b_obj.parcellize(path_to_file1, hemisphere='left')
"""If you want to get the list of all predefined parcellates, use the
`get_parcellates` method which returns a pandas DataFrame with the index, the
name and the color associated to each parcellates
"""
df = b_obj.get_parcellates(path_to_file2)
# print(df)
"""Select only some parcellates. Note that this parcellization is using an
other atlas (Desikan-Killiany atlas)
"""
select = [
'insula', 'paracentral', 'precentral', 'precuneus', 'frontalpole',
'temporalpole', 'fusiform', 'cuneus', 'inferiorparietal',
'inferiortemporal', 'precentral', 'superiorfrontal', 'superiortemporal'
]
"""Instead of using predefined colors inside the annot file, we use some data
"""
data = np.arange(len(select))
b_obj.parcellize(path_to_file2,
hemisphere='right',
select=select,
def create_brain_obj(l_file,
r_file,
activated_areas,
brain_name,
hemisphere='both',
cmap='copper',
vmin=0.,
vmax=0.01):
"""Create BrainObj, add areas, and return object
Input:
--------
l_file: str
r_file: str
brain_areas: pd.DataFrame
dataframe of selected areas, shape ['index', 'values']
brain_name: str
parameter for the BrainObj, in ('white', 'inflated')
hemisphere: str
parameter for the BrainObj, in ('both', 'left', 'right)
cmap: str
maplotlib colomap to use. For pvalues, 'copper' should be used; for estimates, 'coolwarm'.
clim: tuple
colorbar limits. For pvalues, (0,pmax) should be used; for estimates, a symetrical interval.
Output:
--------
b_obj: visbrain.objects.BrainObj
"""
b_obj = BrainObj(brain_name, translucent=False, hemisphere=hemisphere)
annot_data = b_obj.get_parcellates(l_file)
# errors if missing labels - removing labels not in annot_data
annot_select = pd.merge(annot_data.reset_index()[['index', 'Labels']],
activated_areas,
on=['index'],
validate="one_to_one")
annot_select['is_left'] = annot_select['Labels'].apply(
lambda x: x[-1] == 'L')
if annot_select[annot_select.is_left].shape[0] > 0:
b_obj.parcellize(
l_file,
hemisphere='left',
select=annot_select[annot_select.is_left]['Labels'].tolist(),
data=annot_select[annot_select.is_left]['values'].tolist(),
cmap=cmap,
vmin=vmin,
vmax=vmax,
clim=(vmin, vmax))
if annot_select[~annot_select.is_left].shape[0] > 0:
b_obj.parcellize(
r_file,
hemisphere='right',
select=annot_select[~annot_select.is_left]['Labels'].tolist(),
data=annot_select[~annot_select.is_left]['values'].tolist(),
cmap=cmap,
vmin=vmin,
vmax=vmax,
clim=(vmin, vmax))
return b_obj
