解释：target = self.survey.source.target collection = self.survey.source.collection '''Mesh''' # Conductivity in S/m (or resistivity in Ohm m) background_conductivity = 1e-6 air_conductivity = 1e-8 # Permeability in H/m background_permeability = mu_0 air_permeability = mu_0 dh = 0.1 # base cell width dom_width = 20.0 # domain width # num. base cells nbc = 2 ** int(np.round(np.log(dom_width / dh) / np.log(2.0))) # Define the base mesh h = [(dh, nbc)] mesh = TreeMesh([h, h, h], x0="CCC") # Mesh refinement near transmitters and receivers mesh = refine_tree_xyz( mesh, collection.receiver_location, octree_levels=[2, 4], method="radial", finalize=False ) # Refine core mesh region xp, yp, zp = np.meshgrid([-1.5, 1.5], [-1.5, 1.5], [-6, -4]) xyz = np.c_[mkvc(xp), mkvc(yp), mkvc(zp)] mesh = refine_tree_xyz(mesh, xyz, octree_levels=[0, 6], method="box", finalize=False) mesh.finalize() '''Maps''' # Find cells that are active in the forward modeling (cells below surface) ind_active = mesh.gridCC[:, 2] < 0 # Define mapping from model to active cells active_sigma_map = maps.InjectActiveCells(mesh, ind_active, air_conductivity) active_mu_map = maps.InjectActiveCells(mesh, ind_active, air_permeability) # Define model. Models in SimPEG are vector arrays N = int(ind_active.sum()) model = np.kron(np.ones((N, 1)), np.c_[background_conductivity, background_permeability]) ind_cylinder = self.getIndicesCylinder( [target.position[0], target.position[1], target.position[2]], target.radius, target.length, [target.pitch, target.roll], mesh.gridCC ) ind_cylinder = ind_cylinder[ind_active] model[ind_cylinder, :] = np.c_[target.conductivity, target.permeability] # Create model vector and wires model = mkvc(model) wire_map = maps.Wires(("sigma", N), ("mu", N)) # Use combo maps to map from model to mesh sigma_map = active_sigma_map * wire_map.sigma mu_map = active_mu_map * wire_map.mu '''Simulation''' simulation = fdem.simulation.Simulation3DMagneticFluxDensity( mesh, survey=self.survey.survey, sigmaMap=sigma_map, muMap=mu_map, Solver=Solver ) '''Predict''' # Compute predicted data for your model. dpred = simulation.dpred(model) dpred = dpred * 1e9 # Data are organized by frequency, transmitter location, then by receiver. # We had nFreq transmitters and each transmitter had 2 receivers (real and # imaginary component). So first we will pick out the real and imaginary # data bx_real = dpred[0: len(dpred): 6] bx_imag = dpred[1: len(dpred): 6] bx_total = np.sqrt(np.square(bx_real) + np.square(bx_imag)) by_real = dpred[2: len(dpred): 6] by_imag = dpred[3: len(dpred): 6] by_total = np.sqrt(np.square(by_real) + np.square(by_imag)) bz_real = dpred[4: len(dpred): 6] bz_imag = dpred[5: len(dpred): 6] bz_total = np.sqrt(np.square(bz_real) + np.square(bz_imag)) mag_data = np.c_[mkvc(bx_total), mkvc(by_total), mkvc(bz_total)] if collection.SNR is not None: mag_data = self.mag_data_add_noise(mag_data, collection.SNR) data = np.c_[collection.receiver_location, mag_data] # data = (data, ) return data

这段代码是一个 Python 函数，它的作用是进行三维电磁正演模拟，并返回模拟得到的数据。函数中的参数包括一个 Survey 对象和一个 Target 对象，表示要模拟的测量参数和模拟目标。函数主要分为三个部分：

1.建立网格和定义模型属性，包括电导率、磁导率和网格大小等。

2.根据接收器位置和目标位置对网格进行细化，以提高模拟精度。

3.进行电磁正演模拟，并返回模拟得到的数据。

其中，模拟的过程中还进行了噪声的添加，以模拟实际测量中的误差。

相关问题

output['Conductivity'] = {} output['Conductivity']['units'] = 'S/m' n = gc.findnumatoms(datfilename) (molcharges, atomcharges,n) = gc.getmolcharges(datfilename,n) molcharge = gc.molchargedict(molcharges, moltypel, moltype) tsjump = gt.getjump(trjfilename[0]) dt = gt.getdt(logfilename) (V, Lx, Ly, Lz) = gcn.getvolume(trjfilename[0]) (comx, comy, comz, Lx, Ly, Lz, Lx2, Ly2, Lz2) = c.calcCOM(trjfilename,datfilename, ver)解释一下代码

这段代码主要是对一些变量进行赋值和计算。具体解释如下：

1. `output['Conductivity'] = {}`：创建一个字典类型的变量 `output` ，并在其中创建一个名为 `Conductivity` 的子字典。

2. `output['Conductivity']['units'] = 'S/m'`：在 `Conductivity` 子字典中创建一个名为 `units` 的键，并将其值赋为字符串 `'S/m'`。

3. `n = gc.findnumatoms(datfilename)`：调用 `gc` 模块中的函数 `findnumatoms`，传入参数 `datfilename`，并将返回值赋给变量 `n`。

4. `(molcharges, atomcharges,n) = gc.getmolcharges(datfilename,n)`：调用 `gc` 模块中的函数 `getmolcharges`，传入参数 `datfilename` 和 `n`，并将返回值分别赋给变量 `molcharges`、`atomcharges` 和 `n`。

5. `molcharge = gc.molchargedict(molcharges, moltypel, moltype)`：调用 `gc` 模块中的函数 `molchargedict`，传入参数 `molcharges`、`moltypel` 和 `moltype`，并将返回值赋给变量 `molcharge`。

6. `tsjump = gt.getjump(trjfilename[0])`：调用 `gt` 模块中的函数 `getjump`，传入参数 `trjfilename[0]`，并将返回值赋给变量 `tsjump`。

7. `dt = gt.getdt(logfilename)`：调用 `gt` 模块中的函数 `getdt`，传入参数 `logfilename`，并将返回值赋给变量 `dt`。

8. `(V, Lx, Ly, Lz) = gcn.getvolume(trjfilename[0])`：调用 `gcn` 模块中的函数 `getvolume`，传入参数 `trjfilename[0]`，并将返回值分别赋给变量 `V`、`Lx`、`Ly` 和 `Lz`。

9. `(comx, comy, comz, Lx, Ly, Lz, Lx2, Ly2, Lz2) = c.calcCOM(trjfilename,datfilename, ver)`：调用 `c` 模块中的函数 `calcCOM`，传入参数 `trjfilename`、`datfilename` 和 `ver`，并将返回值分别赋给变量 `comx`、`comy`、`comz`、`Lx`、`Ly`、`Lz`、`Lx2`、`Ly2` 和 `Lz2`。

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'

根据你的代码和报错信息来看，mne 模块确实没有 `BEMSurface` 这个属性。根据官方文档，在 mne 的最新版本中，使用 `BEMSurface` 已经被废弃，而改用了 `make_watershed_bem` 函数来创建 BEM 模型。

你可以尝试修改你的代码如下：

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.make_bem_model(inner_skull_vertices, inner_skull_triangles, conductivity=0.01),  # brain
    mne.make_bem_model(outer_skull_vertices, outer_skull_triangles, conductivity=0.016),  # skull
    mne.make_bem_model(scalp_vertices, scalp_triangles, conductivity=0.33),  # skin
]

# Create BEM solution
model = make_watershed_bem(surfs)
solution = mne.make_bem_solution(model)

请注意，我还对 `make_bem_model` 的调用进行了修改，以适应新的 API。希望这能解决你的问题！如果还有其他疑问，请随时提问。