Python库th2_check2_recon版本2.3.2.dev***发布

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资源摘要信息:"Python库 | th2_check2_recon-2.3.2.dev***.tar.gz" 该资源为一个Python库文件包,具体名称为“th2_check2_recon-2.3.2.dev***.tar.gz”,这表明它是一个Python开发的库,且该版本为开发版本(dev),版本号为2.3.2,内部版本号为***。此库的来源为官方发布,意味着该库应当是由某项目的维护者或开发团队官方发布用于测试和开发使用的包。 从提供的文件名和描述来看,“th2_check2_recon”可能指的是一个专注于某种特定功能的库,例如,可能是一个用于处理某种类型的数据校验和重建(reconstruction)的工具或框架。版本号后面的“.dev”表明这是一个开发版本,通常这样的版本包含最新的功能但可能不够稳定,适用于开发人员和早期采用者进行测试和开发工作。 由于该资源是一个压缩包文件,文件名“th2_check2_recon-2.3.2.dev***.tar.gz”遵循了常见的命名规则,其中“.tar.gz”表明该压缩包是使用gzip工具对tar归档文件进行压缩得到的。在Linux系统中,tar通常用来打包文件,而gzip用来对打包后的文件进行压缩,以减小文件大小,方便传输和存储。 要安装这个库,资源描述中提供了一个安装方法的链接,位于CSDN博客,链接为***。虽然这个链接并未直接显示在描述中,但可以推断它可能包含有关如何安装和使用“th2_check2_recon”库的具体指南。 关于【标签】部分,“python 开发语言 Python库”表明该资源与Python编程语言相关,并且它是一个库。Python是一种广泛使用的高级编程语言,它以易读性和简洁的语法著称。在Python中,库是指一系列预构建的功能模块,这些模块可以被其他Python程序导入和使用,以减少重复编码工作,加速开发过程。因此,标签中的“Python库”强调了这个资源是用于Python开发的附加组件。 总结一下,我们讨论的资源是一个Python开发的库,它以压缩包的形式存在,并且目前处于开发版本。这表明它可能包含了最新的实验性功能,适合于那些需要尝试最新特性或者寻求开发帮助的Python开发者。由于其开发版本的性质,使用时需要谨慎,可能会存在不稳定或者bug。安装该库前,开发者应当参考官方提供的安装指南,以确保正确、安全地集成到现有的开发环境中。

import scipy.io import mne from mne.bem import make_watershed_bem # Load .mat files inner_skull = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.inner_skull.mat') outer_skull = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.outer_skull.mat') scalp = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.scalp.mat') print(inner_skull.keys()) # Assuming these .mat files contain triangulated surfaces, we will extract vertices and triangles # This might need adjustment based on the actual structure of your .mat files inner_skull_vertices = inner_skull['Vertices'] inner_skull_triangles = inner_skull['Faces'] outer_skull_vertices = outer_skull['Vertices'] outer_skull_triangles = outer_skull['Faces'] scalp_vertices = scalp['Vertices'] scalp_triangles = scalp['Faces'] # Prepare surfaces for MNE surfs = [ mne.bem.BEMSurface(inner_skull_vertices, inner_skull_triangles, sigma=0.01, id=4), # brain mne.bem.BEMSurface(outer_skull_vertices, outer_skull_triangles, sigma=0.016, id=3), # skull mne.bem.BEMSurface(scalp_vertices, scalp_triangles, sigma=0.33, id=5), # skin ] # Create BEM model model = mne.bem.BEM(surfs, conductivity=[0.3, 0.006, 0.3], is_sphere=False) model.plot(show=False) # Create BEM solution solution = mne.make_bem_solution(model) 运行代码时报错; Traceback (most recent call last): File "E:\pythonProject\MEG\头模型.py", line 24, in <module> mne.bem.BEMSurface(inner_skull_vertices, inner_skull_triangles, sigma=0.01, id=4), # brain AttributeError: module 'mne.bem' has no attribute 'BEMSurface'

2023-07-16 上传

运行代码: import scipy.io import mne from mne.bem import make_watershed_bem import random import string # Load .mat files inner_skull = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.inner_skull.mat') outer_skull = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.outer_skull.mat') scalp = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.scalp.mat') print(inner_skull.keys()) # Assuming these .mat files contain triangulated surfaces, we will extract vertices and triangles # This might need adjustment based on the actual structure of your .mat files inner_skull_vertices = inner_skull['Vertices'] inner_skull_triangles = inner_skull['Faces'] outer_skull_vertices = outer_skull['Vertices'] outer_skull_triangles = outer_skull['Faces'] scalp_vertices = scalp['Vertices'] scalp_triangles = scalp['Faces'] subjects_dir = 'E:\MATLABproject\data\MRI\Visit1_040318' subject = ''.join(random.choices(string.ascii_uppercase + string.ascii_lowercase, k=8)) # Prepare surfaces for MNE # Prepare surfaces for MNE surfs = [ mne.make_bem_model(inner_skull_vertices, inner_skull_triangles, conductivity=[0.01], subjects_dir=subjects_dir), # brain mne.make_bem_model(outer_skull_vertices, outer_skull_triangles, conductivity=[0.016], subjects_dir=subjects_dir), # skull mne.make_bem_model(scalp_vertices, scalp_triangles, conductivity=[0.33], subjects_dir=subjects_dir), # skin ] # Create BEM solution model = make_watershed_bem(surfs) solution = mne.make_bem_solution(model) 时报错: Traceback (most recent call last): File "E:\pythonProject\MEG\头模型.py", line 30, in <module> mne.make_bem_model(inner_skull_vertices, inner_skull_triangles, conductivity=[0.01], subjects_dir=subjects_dir), # brain File "<decorator-gen-68>", line 12, in make_bem_model File "E:\anaconda\envs\pythonProject\lib\site-packages\mne\bem.py", line 712, in make_bem_model subject_dir = op.join(subjects_dir, subject) File "E:\anaconda\envs\pythonProject\lib\ntpath.py", line 117, in join genericpath._check_arg_types('join', path, *paths) File "E:\anaconda\envs\pythonProject\lib\genericpath.py", line 152, in _check_arg_types raise TypeError(f'{funcname}() argument must be str, bytes, or ' TypeError: join() argument must be str, bytes, or os.PathLike object, not 'ndarray' 进程已结束,退出代码1

2023-07-20 上传