默认情况下,plot_glass_brain使用一种名为'ortho'的显示模式,它会产生三个投影,等价于在plot_glass_brain中指定display_mode='ortho'。 from nilearn import plotting from nilearn.plotting import plot_glass_brain # 整个大脑的矢状切面和地图的阈值是3 plot_glass_brain(stat_img, threshold=3) plot_stat_ma...
>>> from nilearn import plotting >>> plotting.plot_stat_map(img, output_file='pretty_brain.png') 在这种情况下,显示将自动关闭,并且绘图功能返回无。 绘图功能返回的显示对象具有savefig方法,可用于将绘图保存到图像文件: >>> from nilearn import plotting >>> display = plotting.plot_stat_map(img) ...
warnings.filterwarnings("ignore")# Plot allICAcomponents togetherplot_prob_atlas(components_img,title='All ICA components') 3.2将所有的ICA组件单独绘制 代码语言:javascript 代码运行次数:0 运行 AI代码解释 from nilearn.imageimportiter_img from nilearn.plottingimportplot_stat_map,showimportwarnings ...
""" # 设置阈值来绘制效果图这里的阈值设置为3 threshold=3 """ plotting.plot_stat_map(tmap_filename, threshold=3) 可视化4D文件 代码语言:javascript 代码运行次数:0 运行 AI代码解释 rsn = datasets.fetch_atlas_smith_2009()['rsn10'] print(rsn) 代码语言:javascript 代码运行次数:0 运行 AI代码解释...
plotting.plot_stat_map(tmap_filename) 1. 2. 3. """ # 设置阈值来绘制效果图 这里的阈值设置为3 threshold=3 """ plotting.plot_stat_map(tmap_filename, threshold=3) 1. 2. 3. 4. 5. 6. 7. 可视化4D文件 rsn = datasets.fetch_atlas_smith_2009()['rsn10'] ...
plotting.plot_stat_map(tmap_filename) """ # 设置阈值来绘制效果图 这里的阈值设置为3 threshold=3 """plotting.plot_stat_map(tmap_filename, threshold=3) 可视化4D文件 rsn = datasets.fetch_atlas_smith_2009()['rsn10']print(rsn) """
plot_prob_atlas(components_img, title='All ICA components') 1. 2. 3. 4. 5. 3.2将所有的ICA组件单独绘制 from nilearn.image import iter_img from nilearn.plotting import plot_stat_map, show import warnings warnings.filterwarnings("ignore") ...
fromnilearn.plottingimportplot_prob_atlasimportwarnings warnings.filterwarnings("ignore")# Plot all ICA components togetherplot_prob_atlas(components_img, title='All ICA components') 3.2将所有的ICA组件单独绘制 fromnilearn.imageimportiter_imgfromnilearn.plottingimportplot_stat_map, showimportwarnings ...
from nilearn.plotting import plot_stat_map from nilearn.image import iter_img dataset = load_dynacomp() func = dataset.func1 n_components = 20 canica = CanICA(n_components=n_components, mask=dataset.mask, smoothing_fwhm=None, do_cca=True, threshold=3., ...
the gain of the gamble.fromnilearn.decodingimportSpaceNetRegressordecoder=SpaceNetRegressor(mask=mask_img,penalty="tv-l1",eps=1e-1,# prefer large alphasmemory="cache")decoder.fit(zmaps,object_category)# fit# Visualize TV-L1 weightsimportmatplotlib.pyplotaspltfromnilearn.plottingimportplot_stat_map...