非官方中文翻译本页译自 MNE-Python 官方文档：查看英文原文 ↗。文档版权归 MNE Developers 所有（BSD-3-Clause）。

说明

转到末尾下载完整示例代码。

开始使用 `mne.Report`#

mne.Report 是一种创建数据交互式 HTML 摘要的方式。这些报告可以为一个或多个参与者展示许多不同的可视化结果。一个常见用例是创建诊断性摘要，以便在处理流程的不同阶段检查数据质量。报告可以展示诸如每个预处理步骤前后的数据图、坏段剔除统计、叠加 BEM 外壳的 MRI 切片，直至估计的皮层活动图等内容。

与 Jupyter notebook 相比，mne.Report 更易于部署，因为它生成的 HTML 页面是自包含的，不需要运行 Python 环境。不过，它的灵活性较低，因为你无法在浏览器中直接修改代码并重新运行。本教程介绍构建报告的基础知识。按照惯例，我们先导入所需的模块和数据：

# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import tempfile
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import scipy.ndimage

import mne

data_path = Path(mne.datasets.sample.data_path(verbose=False))
sample_dir = data_path / "MEG" / "sample"
subjects_dir = data_path / "subjects"

创建 HTML 报告的基本流程是：先实例化 Report 类，然后使用其众多方法中的一个或多个，每次添加一个元素。

你也可以使用 parse_folder() 方法选择要包含在报告中的特定文件。稍后再详细介绍。

添加原始数据#

可以通过 mne.Report.add_raw() 方法添加原始数据。它既可以处理原始文件路径，也可以处理 Raw 对象，并会生成——除其他输出外——一个滑块，让你可以浏览数据中 10 个等间隔的 1 秒片段：

警告

在下面的示例中，我们将原始数据裁剪到 60 秒，仅仅是为了加快处理速度；通常并不建议这样做！

raw_path = sample_dir / "sample_audvis_filt-0-40_raw.fif"
raw = mne.io.read_raw(raw_path)
raw.pick(picks=["eeg", "eog", "stim"]).crop(tmax=60).load_data()

report = mne.Report(title="Raw example")
# This method also accepts a path, e.g., raw=raw_path
report.add_raw(raw=raw, title="Raw", psd=False)  # omit PSD plot
report.save("report_raw.html", overwrite=True)

The generated HTML document.

Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Reading 0 ... 9009  =      0.000 ...    59.999 secs...
Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Using matplotlib as 2D backend.
Using qt as 2D backend.
Saving report to : /home/circleci/project/tutorials/intro/report_raw.html

添加事件#

可以通过 mne.Report.add_events() 添加事件。你还需要提供记录期间使用的采样频率；该信息用于生成有意义的时间轴。

events_path = sample_dir / "sample_audvis_filt-0-40_raw-eve.fif"
events = mne.find_events(raw=raw)
sfreq = raw.info["sfreq"]

report = mne.Report(title="Events example")
report.add_events(events=events_path, title="Events from Path", sfreq=sfreq)
report.add_events(events=events, title='Events from "events"', sfreq=sfreq)
report.save("report_events.html", overwrite=True)

The generated HTML document.

Finding events on: STI 014
86 events found on stim channel STI 014
Event IDs: [ 1  2  3  4  5 32]
Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Saving report to : /home/circleci/project/tutorials/intro/report_events.html

添加 Epochs#

可以通过 mne.Report.add_epochs() 添加 Epochs。请注意，尽管该方法也接受 epochs 文件路径，但在下面的示例中，我们只添加从原始数据即时创建的 epochs。为了演示 epochs 元数据的呈现方式，我们还会添加一些元数据。

event_id = {
    "auditory/left": 1,
    "auditory/right": 2,
    "visual/left": 3,
    "visual/right": 4,
    "face": 5,
    "buttonpress": 32,
}

metadata, _, _ = mne.epochs.make_metadata(
    events=events, event_id=event_id, tmin=-0.2, tmax=0.5, sfreq=raw.info["sfreq"]
)
epochs = mne.Epochs(raw=raw, events=events, event_id=event_id, metadata=metadata)

report = mne.Report(title="Epochs example")
report.add_epochs(epochs=epochs, title='Epochs from "epochs"')
report.save("report_epochs.html", overwrite=True)

The generated HTML document.

Adding metadata with 7 columns
86 matching events found
Setting baseline interval to [-0.19979521315838786, 0.0] s
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 1)
1 projection items activated
Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Using data from preloaded Raw for 86 events and 106 original time points ...
0 bad epochs dropped
    Using multitaper spectrum estimation with 7 DPSS windows
Plotting power spectral density (dB=True).
Averaging across epochs before plotting...
Saving report to : /home/circleci/project/tutorials/intro/report_epochs.html

添加 Evoked#

可以通过 mne.Report.add_evokeds() 添加诱发电位（Evoked）数据。默认情况下，每个 evoked 的 Evoked.comment 属性将用作标题。我们可以通过 titles 参数指定自定义标题。同样，该方法也接受磁盘上存储的 evoked 文件路径；不过在下面的示例中，我们首先手动加载 evokeds，因为我们只想将其中的一部分添加到报告中。这些 evokeds 未进行基线校正，因此我们也应用基线校正。最后，通过提供（可选的）噪声协方差，我们可以添加使用该协方差矩阵进行“白化”的 evoked 图。

默认情况下，该方法会在 21 个等间隔时间点（如果数据包含的时间点更少，则更少）生成地形图。我们可以通过 n_time_points 参数进行调整。

evoked_path = sample_dir / "sample_audvis-ave.fif"
cov_path = sample_dir / "sample_audvis-cov.fif"

evokeds = mne.read_evokeds(evoked_path, baseline=(None, 0))
evokeds_subset = evokeds[:2]  # The first two
for evoked in evokeds_subset:
    evoked.pick("eeg")  # just for speed of plotting

report = mne.Report(title="Evoked example")
report.add_evokeds(
    evokeds=evokeds_subset,
    titles=["evoked 1", "evoked 2"],  # Manually specify titles
    noise_cov=cov_path,
    n_time_points=5,
)
report.save("report_evoked.html", overwrite=True)

The generated HTML document.

Reading /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-ave.fif ...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Left Auditory)
        0 CTF compensation matrices available
        nave = 55 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Right Auditory)
        0 CTF compensation matrices available
        nave = 61 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Left visual)
        0 CTF compensation matrices available
        nave = 67 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Right visual)
        0 CTF compensation matrices available
        nave = 58 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)
Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
    366 x 366 full covariance (kind = 1) found.
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
Computing rank from covariance with rank=None
    Using tolerance 4.7e-14 (2.2e-16 eps * 59 dim * 3.6  max singular value)
    Estimated rank (eeg): 58
    EEG: rank 58 computed from 59 data channels with 1 projector
    Created an SSP operator (subspace dimension = 1)
Computing rank from covariance with rank={'eeg': 58}
    Setting small EEG eigenvalues to zero (without PCA)
    Created the whitener using a noise covariance matrix with rank 58 (1 small eigenvalues omitted)
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
Computing rank from covariance with rank=None
    Using tolerance 4.7e-14 (2.2e-16 eps * 59 dim * 3.6  max singular value)
    Estimated rank (eeg): 58
    EEG: rank 58 computed from 59 data channels with 1 projector
    Created an SSP operator (subspace dimension = 1)
Computing rank from covariance with rank={'eeg': 58}
    Setting small EEG eigenvalues to zero (without PCA)
    Created the whitener using a noise covariance matrix with rank 58 (1 small eigenvalues omitted)
Saving report to : /home/circleci/project/tutorials/intro/report_evoked.html

添加协方差#

可以通过 mne.Report.add_covariance() 添加（噪声）协方差对象。该方法接受 Covariance 对象和磁盘上的文件路径。它还要求我们传入 Info 对象或读取测量信息的文件路径，以及一个标题。

cov_path = sample_dir / "sample_audvis-cov.fif"

report = mne.Report(title="Covariance example")
report.add_covariance(cov=cov_path, info=raw_path, title="Covariance")
report.save("report_cov.html", overwrite=True)

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
    366 x 366 full covariance (kind = 1) found.
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
Computing rank from covariance with rank=None
    Using tolerance 2.5e-14 (2.2e-16 eps * 102 dim * 1.1  max singular value)
    Estimated rank (mag): 102
    MAG: rank 102 computed from 102 data channels with 0 projectors
Computing rank from covariance with rank=None
    Using tolerance 2.6e-12 (2.2e-16 eps * 204 dim * 56  max singular value)
    Estimated rank (grad): 204
    GRAD: rank 204 computed from 204 data channels with 0 projectors
Computing rank from covariance with rank=None
    Using tolerance 4.8e-14 (2.2e-16 eps * 60 dim * 3.6  max singular value)
    Estimated rank (eeg): 60
    EEG: rank 60 computed from 60 data channels with 0 projectors
Saving report to : /home/circleci/project/tutorials/intro/report_cov.html

添加投影向量#

可以通过 mne.Report.add_projs() 添加投影向量。该方法需要一个 Info 对象（或其路径）和一个标题。Info 中的投影器将被可视化。你也可以提供 Projection 对象列表或磁盘上存储的投影器路径。在这种情况下，通道信息从 Info 读取，但其中可能包含的投影器将被忽略；相反，只会绘制显式传入的投影器。

ecg_proj_path = sample_dir / "sample_audvis_ecg-proj.fif"
report = mne.Report(title="Projectors example")
report.add_projs(info=raw_path, title="Projs from info")

# Now a joint plot
events = mne.read_events(sample_dir / "sample_audvis_ecg-eve.fif")
raw_full = mne.io.read_raw(sample_dir / "sample_audvis_raw.fif").crop(0, 60).load_data()
ecg_evoked = mne.Epochs(
    raw=raw_full,
    events=events,
    tmin=-0.5,
    tmax=0.5,
    baseline=(None, None),
    on_outside="ignore",
).average()
report.img_max_width = None  # do not constrain image width
report.add_projs(
    info=ecg_evoked,
    projs=ecg_proj_path,
    title="ECG projs from path",
    joint=True,  # use joint version of the plot
)
report.save("report_projs.html", overwrite=True)
del raw_full, events, ecg_evoked

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.
Reading 0 ... 36037  =      0.000 ...    60.000 secs...
Not setting metadata
268 matching events found
Setting baseline interval to [-0.49948803289596966, 0.49948803289596966] s
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 3)
3 projection items activated
    Read a total of 6 projection items:
        ECG-planar-999--0.200-0.400-PCA-01 (1 x 203)  idle
        ECG-planar-999--0.200-0.400-PCA-02 (1 x 203)  idle
        ECG-axial-999--0.200-0.400-PCA-01 (1 x 102)  idle
        ECG-axial-999--0.200-0.400-PCA-02 (1 x 102)  idle
        ECG-eeg-999--0.200-0.400-PCA-01 (1 x 59)  idle
        ECG-eeg-999--0.200-0.400-PCA-02 (1 x 59)  idle
2 projection items deactivated
2 projection items deactivated
2 projection items deactivated
Saving report to : /home/circleci/project/tutorials/intro/report_projs.html

添加 ICA#

可以通过 mne.Report.add_ica() 添加 ICA 对象。除了参数 ica（接受 ICA 实例或磁盘上存储的 ICA 对象路径）和 title 之外，还有第三个必需参数 inst。inst 用于指定 Raw 或 Epochs 对象，以生成 ICA 属性图和展示 ICA 清洗效果的叠加图。如果你只想生成 ICA 成分地形图，请显式传入 inst=None。

说明

mne.Report.add_ica() 仅适用于已拟合的 ICA。

你可以通过传入 picks 选择要为其生成地形图和属性图的成分。默认情况下，将显示所有成分。还可以通过 ecg_evoked 和 eog_evoked 传入基于 ECG 和 EOG 事件的诱发信号。这样你可以直接看到 ICA 成分移除对这些伪迹信号的影响。find_bads_ecg() 和 find_bads_eog() 产生的伪迹检测分数可以分别通过 ecg_scores 和 eog_scores 参数传入，从而可视化每个 ICA 成分的分数。

最后，通过传入 n_jobs，你可以启用并行执行，从而大幅加快属性图的生成速度。若要在报告中额外包含 ICA 源时间过程，请传入 plot_sources=True。这要求提供 inst。

警告

在下面的示例中，我们请求估计少量 ICA 成分，将假设 ICA 收敛的阈值设得非常宽松，并且只可视化其中 2 个成分。这样做都是为了大幅减少本教程的处理时间，通常不建议在实际数据分析中这样做。

ica = mne.preprocessing.ICA(
    n_components=5,  # fit 5 ICA components
    fit_params=dict(tol=0.01),  # assume very early on that ICA has converged
    rng=97,
)

ica.fit(inst=raw)

# create epochs based on EOG events, find EOG artifacts in the data via pattern
# matching, and exclude the EOG-related ICA components
eog_epochs = mne.preprocessing.create_eog_epochs(raw=raw)
eog_components, eog_scores = ica.find_bads_eog(
    inst=eog_epochs,
    ch_name="EEG 001",  # a channel close to the eye
    threshold=1,  # lower than the default threshold
)
ica.exclude = eog_components

report = mne.Report(title="ICA example")
report.add_ica(
    ica=ica,
    title="ICA cleaning",
    picks=ica.exclude,  # plot the excluded EOG components
    inst=raw,
    plot_sources=True,
    eog_evoked=eog_epochs.average(),
    eog_scores=eog_scores,
    n_jobs=None,  # could be increased!
)
report.save("report_ica.html", overwrite=True)

The generated HTML document.

Fitting ICA to data using 59 channels (please be patient, this may take a while)
Selecting by number: 5 components
Fitting ICA took 0.1s.
Using EOG channel: EOG 061
EOG channel index for this subject is: [69]
Filtering the data to remove DC offset to help distinguish blinks from saccades
Selecting channel EOG 061 for blink detection
Setting up band-pass filter from 1 - 10 Hz

FIR filter parameters
---------------------
Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:
- Windowed frequency-domain design (firwin2) method
- Hann window
- Lower passband edge: 1.00
- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)
- Upper passband edge: 10.00 Hz
- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)
- Filter length: 1502 samples (10.003 s)

Now detecting blinks and generating corresponding events
Found 10 significant peaks
Number of EOG events detected: 10
Not setting metadata
10 matching events found
No baseline correction applied
Created an SSP operator (subspace dimension = 1)
Using data from preloaded Raw for 10 events and 151 original time points ...
0 bad epochs dropped
Using EOG channel: EEG 001
Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Applying ICA to Raw instance
    Transforming to ICA space (5 components)
    Zeroing out 3 ICA components
    Projecting back using 59 PCA components
Using matplotlib as 2D backend.
Using qt as 2D backend.
    Using multitaper spectrum estimation with 7 DPSS windows
Not setting metadata
30 matching events found
No baseline correction applied
0 projection items activated
    Using multitaper spectrum estimation with 7 DPSS windows
Not setting metadata
30 matching events found
No baseline correction applied
0 projection items activated
    Using multitaper spectrum estimation with 7 DPSS windows
Not setting metadata
30 matching events found
No baseline correction applied
0 projection items activated
Using matplotlib as 2D backend.
Creating RawArray with float64 data, n_channels=4, n_times=9010
    Range : 6450 ... 15459 =     42.956 ...   102.954 secs
Ready.
Using qt as 2D backend.
Saving report to : /home/circleci/project/tutorials/intro/report_ica.html

添加带 BEM 的 MRI#

可以通过 mne.Report.add_bem() 添加叠加边界元模型（BEM）表面轨迹的 MRI 切片。你只需传入 FreeSurfer 受试者名称和 subjects 目录，以及一个标题。为减小生成文件的大小，你可以传入 decim 参数以仅包含每第 n 个体积切片，并使用 width 指定生成图形的宽度（以像素为单位）。

report = mne.Report(title="BEM example")
report.add_bem(
    subject="sample",
    subjects_dir=subjects_dir,
    title="MRI & BEM",
    decim=40,
    width=256,
)
report.save("report_mri_and_bem.html", overwrite=True)

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Using surface: /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/inner_skull.surf
Using surface: /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/outer_skull.surf
Using surface: /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/outer_skin.surf
Saving report to : /home/circleci/project/tutorials/intro/report_mri_and_bem.html

添加配准#

传感器对齐（通过“配准”获得的 head -> mri 变换）可以通过 mne.Report.add_trans() 可视化。该方法期望变换以 Transform 对象或 trans.fif 文件路径的形式提供，同时需要 FreeSurfer 受试者名称、subjects 目录和标题。alpha 参数可用于控制头部的透明度，值为 1 表示完全不透明。

trans_path = sample_dir / "sample_audvis_raw-trans.fif"

report = mne.Report(title="Coregistration example")
report.add_trans(
    trans=trans_path,
    info=raw_path,
    subject="sample",
    subjects_dir=subjects_dir,
    alpha=1.0,
    title="Coregistration",
)
report.save("report_coregistration.html", overwrite=True)

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
Using outer_skin.surf for head surface.
Getting helmet for system 306m
Channel types:: grad: 203, mag: 102, eeg: 59
Using surface from /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-head.fif.
Saving report to : /home/circleci/project/tutorials/intro/report_coregistration.html

添加正演解#

可以通过将 Forward 对象或磁盘上存储的正演解路径传给 mne.Report.add_forward() 来添加正演解（“导联场”）。

fwd_path = sample_dir / "sample_audvis-meg-oct-6-fwd.fif"

report = mne.Report(title="Forward solution example")
report.add_forward(
    forward=fwd_path, title="Forward solution", plot=True, subjects_dir=subjects_dir
)
report.save("report_forward_sol.html", overwrite=True)

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Reading forward solution from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-oct-6-fwd.fif...
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    2 source spaces read
    Desired named matrix (kind = 3523 (FIFF_MNE_FORWARD_SOLUTION_GRAD)) not available
    Read MEG forward solution (7498 sources, 306 channels, free orientations)
    Source spaces transformed to the forward solution coordinate frame
Using outer_skin.surf for head surface.
Saving report to : /home/circleci/project/tutorials/intro/report_forward_sol.html

添加 InverseOperator#

可以通过 mne.Report.add_inverse_operator() 添加逆算子。该方法期望一个 InverseOperator 对象或磁盘上存储的对应路径，以及一个标题。

inverse_op_path = sample_dir / "sample_audvis-meg-oct-6-meg-inv.fif"

report = mne.Report(title="Inverse operator example")
report.add_inverse_operator(
    inverse_operator=inverse_op_path,
    title="Inverse operator",
    plot=True,
    subjects_dir=subjects_dir,
)
report.save("report_inverse_op.html", overwrite=True)

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Reading inverse operator decomposition from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif...
    Reading inverse operator info...
    [done]
    Reading inverse operator decomposition...
    [done]
    305 x 305 full covariance (kind = 1) found.
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Noise covariance matrix read.
    22494 x 22494 diagonal covariance (kind = 2) found.
    Source covariance matrix read.
    22494 x 22494 diagonal covariance (kind = 6) found.
    Orientation priors read.
    22494 x 22494 diagonal covariance (kind = 5) found.
    Depth priors read.
    Did not find the desired covariance matrix (kind = 3)
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    2 source spaces read
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Source spaces transformed to the inverse solution coordinate frame
Using outer_skin.surf for head surface.
Saving report to : /home/circleci/project/tutorials/intro/report_inverse_op.html

添加 SourceEstimate#

可以通过 mne.Report.add_stc() 添加逆解（也称为源估计或源时间过程，STC）。该方法期望一个 SourceEstimate、相应的 FreeSurfer 受试者名称和 subjects 目录，以及一个标题。默认情况下，它会在 51 个等间隔时间点（如果数据包含的时间点更少，则更少）生成快照。我们可以通过 n_time_points 参数进行调整。

stc_path = sample_dir / "sample_audvis-meg"

report = mne.Report(title="Source estimate example")
report.add_stc(
    stc=stc_path,
    subject="sample",
    subjects_dir=subjects_dir,
    title="Source estimate",
    n_time_points=2,  # few for speed
)
report.save("report_inverse_sol.html", overwrite=True)

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Using control points [ 5.01632618  6.06303297 21.63565434]
Saving report to : /home/circleci/project/tutorials/intro/report_inverse_sol.html

添加源代码（例如 Python 脚本）#

可以通过 mne.Report.add_code() 将代码或脚本（例如你用于分析的脚本）添加到报告中。代码块将自动进行语法高亮。你可以传入包含相应代码片段的字符串，或文件路径。如果传入路径，它必须是 pathlib.Path 对象（而不是字符串），否则将被视为代码字面量。

或者，你可以通过 language 参数指定用于语法高亮的编程语言。默认情况下，我们假定提供的代码是 Python。

mne_init_py_path = Path(mne.__file__)  # __init__.py in the MNE-Python root
mne_init_py_content = mne_init_py_path.read_text(encoding="utf-8")

report = mne.Report(title="Code example")
report.add_code(code=mne_init_py_path, title="Code from Path")
report.add_code(code=mne_init_py_content, title="Code from string")

report.save("report_code.html", overwrite=True)

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Saving report to : /home/circleci/project/tutorials/intro/report_code.html

添加自定义图形#

可以通过 add_figure() 添加自定义 Matplotlib 图形。必需参数是图形和标题。你还可以选择添加显示在图形下方的图注。你还可以指定将从图形生成的图像文件的图像格式，以便将其嵌入 HTML 报告。

x = np.linspace(start=0, stop=10, num=100)
y = x**2

fig, ax = plt.subplots()
ax.plot(x, y, ls="--", lw=2, color="blue", label="my function")
ax.set_xlabel("x")
ax.set_ylabel("f(x)")
ax.legend()

report = mne.Report(title="Figure example")
report.add_figure(
    fig=fig,
    title="A custom figure",
    caption="A blue dashed line reaches up into the sky …",
    image_format="PNG",
)
report.save("report_custom_figure.html", overwrite=True)
plt.close(fig)

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Saving report to : /home/circleci/project/tutorials/intro/report_custom_figure.html

可以通过 section 参数将多个图形分组成单个 section。

fig_1, ax_1 = plt.subplots()
ax_1.plot([1, 2, 3])

fig_2, ax_2 = plt.subplots()
ax_2.plot([3, 2, 1])

section = "Section example"

report = mne.Report(title="Figure section example")
report.add_figure(fig=fig_1, title="Figure 1", section=section, tags="fig-1")
report.add_figure(fig=fig_2, title="Figure 2", section=section, tags="fig-2")
report.save("report_custom_figure_sections.html", overwrite=True)
plt.close(fig_1)
plt.close(fig_2)

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Saving report to : /home/circleci/project/tutorials/intro/report_custom_figure_sections.html

mne.Report.add_figure() 方法也可以一次添加多个图形。在这种情况下，会出现一个滑块，让用户直观地浏览这些图形。为此，你需要提供图形集合、标题，以及可选的图注集合。

在下面的示例中，我们将把 MNE 徽标作为 Matplotlib 图形读取，并以不同角度旋转它。每个旋转后的图形及其相应图注都会被添加到一个列表中，然后用于创建滑块。

mne_logo_path = Path(mne.__file__).parent / "icons" / "mne_icon-cropped.png"
fig_array = plt.imread(mne_logo_path)
rotation_angles = np.linspace(start=0, stop=360, num=8, endpoint=False)

figs = []
captions = []
for angle in rotation_angles:
    # Rotate and remove some rounding errors to avoid Matplotlib warnings
    fig_array_rotated = scipy.ndimage.rotate(input=fig_array, angle=angle)
    fig_array_rotated = fig_array_rotated.clip(min=0, max=1)

    # Create the figure
    fig, ax = plt.subplots(figsize=(3, 3), layout="constrained")
    ax.imshow(fig_array_rotated)
    ax.set_axis_off()

    # Store figure and caption
    figs.append(fig)
    captions.append(f"Rotation angle: {round(angle, 1)}°")

report = mne.Report(title="Multiple figures example")
report.add_figure(fig=figs, title="Fun with figures! 🥳", caption=captions)
report.save("report_custom_figures.html", overwrite=True)
for fig in figs:
    plt.close(fig)
del figs

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Saving report to : /home/circleci/project/tutorials/intro/report_custom_figures.html

添加图像文件#

可以通过 mne.Report.add_image() 将现有图像（例如照片、截图、草图等）添加到报告中。支持的图像格式包括 JPEG、PNG、GIF 和 SVG（可能还支持其他格式）。与 Matplotlib 图形一样，你可以指定显示在图像下方的图注。

report = mne.Report(title="Image example")
report.add_image(
    image=mne_logo_path, title="MNE", caption="Powered by 🧠 🧠 🧠 around the world!"
)
report.save("report_custom_image.html", overwrite=True)

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Saving report to : /home/circleci/project/tutorials/intro/report_custom_image.html

使用标签#

每个 add_* 方法都接受关键字参数 tags，可用于传入一个或多个标签，以关联到相应的内容元素。默认情况下，每个 add_* 方法都会添加一个描述数据类型的标签，例如 evoked 或 source-estimate。查看 HTML 报告时，可以使用“按标签筛选”下拉菜单以交互方式显示或隐藏具有特定标签的内容。例如，如果你添加了这些标签，就可以只查看 evoked 或 participant-001 数据。可见标签显示为蓝色背景，隐藏标签显示为灰色背景。

要切换所有标签的可见性，请使用“按标签筛选”下拉菜单中相应的复选框，或按 T 键。

report = mne.Report(title="Tags example")
report.add_image(
    image=mne_logo_path,
    title="MNE Logo",
    tags=("image", "mne", "logo", "open-source"),
)
report.save("report_tags.html", overwrite=True)

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Saving report to : /home/circleci/project/tutorials/intro/report_tags.html

编辑已保存的报告#

保存为 HTML 是只写操作，这意味着我们无法将 .html 文件读回为 Report 对象。为了能够在报告不再位于活动 Python 会话的内存中后仍能编辑它，请将其保存为 HDF5 文件而不是 HTML：

report = mne.Report(title="Saved report example", verbose=True)
report.add_image(image=mne_logo_path, title="MNE 1")
report.save("report_partial.hdf5", overwrite=True)

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Saving report to : /home/circleci/project/tutorials/intro/report_partial.hdf5

保存的报告可以读回并修改或补充。这样，例如可以在处理流程中运行多个脚本，每个脚本向现有报告添加新内容。

report_from_disk = mne.open_report("report_partial.hdf5")
report_from_disk.add_image(image=mne_logo_path, title="MNE 2")
report_from_disk.save("report_partial.hdf5", overwrite=True)

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Overwriting existing file.
Saving report to : /home/circleci/project/tutorials/intro/report_partial.hdf5

为了更加方便，mne.Report 可以用作上下文管理器（注意 with 语句）：

with mne.open_report("report_partial.hdf5") as report:
    report.add_image(image=mne_logo_path, title="MNE 3")
    report.save("report_final.html", overwrite=True)

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Saving report to : /home/circleci/project/tutorials/intro/report_final.html
Overwriting existing file.
Saving report to : /home/circleci/project/tutorials/intro/report_partial.hdf5

使用上下文管理器时，离开代码块后，更新后的报告也会自动保存回 report.h5。

添加整个文件夹中的文件#

我们还提供了一种方法，可以一次性将整个文件夹中的文件添加到报告中，而无需为每个文件调用上述各个 add_* 方法。这种方法虽然方便，但在内容顺序、标签、标题等方面灵活性较低。

在开始使用 mne.Report 之前，请确保要渲染的文件遵循 MNE 定义的文件名约定：

数据对象	文件名约定（以以下内容结尾）
`Raw`	`-raw.fif(.gz)`、`-raw_sss.fif(.gz)`、`-raw_tsss.fif(.gz)`、`_meg.fif(.gz)`、`_eeg.fif(.gz)`、`_ieeg.fif(.gz)`
events	`-eve.fif(.gz)`
`Epochs`	`-epo.fif(.gz)`
`Evoked`	`-ave.fif(.gz)`
`Covariance`	`-cov.fif(.gz)`
`Projection`	`-proj.fif(.gz)`
`Transform`	`-trans.fif(.gz)`
`Forward`	`-fwd.fif(.gz)`
`InverseOperator`	`-inv.fif(.gz)`
`SourceEstimate`	`-lh.stc`、`-rh.stc`

或者，文件名中的连字符 - 可以替换为下划线 _（.stc 文件除外）。

在第一个示例中，我们将通过向 parse_folder() 传入模式 *raw.fif，为样本数据集中所有包含原始数据的 .fif 文件生成一个精简报告。我们将省略 Report 构造函数中的 subject 和 subjects_dir 参数，但也会向 parse_folder() 方法传入 render_bem=False——否则，由于不知道受试者，我们会收到无法渲染 MRI 和 trans 文件的警告。为了在本教程中节省一些处理时间，我们还将通过传入 raw_butterfly=False 禁用原始数据的蝶形图渲染。

包含哪些文件取决于传给 parse_folder() 的 pattern 参数，以及提供给 Report 构造函数的 subject 和 subjects_dir 参数。

report = mne.Report(title="parse_folder example")
report.parse_folder(
    data_path=data_path, pattern="*raw.fif", render_bem=False, raw_butterfly=False
)
report.save("report_parse_folder_basic.html", overwrite=True)

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/ernoise_raw.fif...
Isotrak not found
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 19800 ... 85867 =     32.966 ...   142.965 secs
Ready.
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Iterating over 3 potential files (this may take some
Rendering: /home/circleci/mne_data/MNE-sample-data/MEG/sample/ernoise_raw.fif
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/ernoise_raw.fif...
Isotrak not found
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 19800 ... 85867 =     32.966 ...   142.965 secs
Ready.
Rendering: /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.
Rendering: /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Saving report to : /home/circleci/project/tutorials/intro/report_parse_folder_basic.html

默认情况下，为加快报告生成速度，不会显示原始数据的功率谱密度和 SSP 投影器。你可以通过向 Report 构造函数传入 raw_psd=True 和 projs=True 来添加它们。与上一个示例一样，我们将通过传入 raw_butterfly=False 省略蝶形图。最后，我们还要细化模式，只选择滤波后的原始记录（省略未滤波数据和空房间噪声记录）。

pattern = "sample_audvis_filt-0-40_raw.fif"
report = mne.Report(title="parse_folder example 2", raw_psd=True, projs=True)
report.parse_folder(
    data_path=data_path, pattern=pattern, render_bem=False, raw_butterfly=False
)
report.save("report_parse_folder_raw_psd_projs.html", overwrite=True)

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Iterating over 1 potential files (this may take some
Rendering: /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Effective window size : 13.639 (s)
Plotting power spectral density (dB=True).
Saving report to : /home/circleci/project/tutorials/intro/report_parse_folder_raw_psd_projs.html

这一次，我们将传入特定的 subject 和 subjects_dir（即使样本数据集中只有一个受试者），并移除 render_bem=False 参数，以便查看 MRI 切片（如果可用，还会叠加 BEM 轮廓）。由于这计算量较大，我们还将传入 mri_decim 参数以照顾我们的文档服务器，并跳过对 .fif 文件的处理。

report = mne.Report(
    title="parse_folder example 3", subject="sample", subjects_dir=subjects_dir
)
report.parse_folder(data_path=data_path, pattern="", mri_decim=40)
report.save("report_parse_folder_mri_bem.html", overwrite=True)

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Iterating over 0 potential files (this may take some
Rendering BEM
Using surface: /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/inner_skull.surf
Using surface: /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/outer_skull.surf
Using surface: /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/outer_skin.surf
Saving report to : /home/circleci/project/tutorials/intro/report_parse_folder_mri_bem.html

现在我们来看看 Report 如何处理 Evoked 数据（我们将跳过 MRI 以节省计算时间）。

本示例使用的 MNE 样本数据集尚未进行基线校正；因此，我们现在为报告应用基线校正！

要请求基线校正，请向 Report 传入 baseline 参数，该参数应为一个包含基线期起始和结束时间的元组。更多细节请参阅 apply_baseline 的文档。这里，我们将对从时间区间起点到时间点零的基线期应用基线校正。

最后，我们还想渲染“白化”后的 evoked 数据。白化要求我们通过 Report 的 cov_fname 参数指定协方差矩阵文件的路径。

现在，让我们把所有这些放在一起！这里我们使用临时目录以提高速度，从而只使用 EEG 通道渲染单个 Evoked 实例。

baseline = (None, 0)
cov_fname = sample_dir / "sample_audvis-cov.fif"
pattern = "sample_audvis-ave.fif"
evoked = mne.read_evokeds(sample_dir / pattern)[0].pick("eeg").decimate(4)
report = mne.Report(
    title="parse_folder example 4", baseline=baseline, cov_fname=cov_fname
)
with tempfile.TemporaryDirectory() as path:
    evoked.save(Path(path) / pattern)
    report.parse_folder(
        path, pattern=pattern, render_bem=False, n_time_points_evokeds=5
    )
report.save("report_parse_folder_evoked.html", overwrite=True)

The generated HTML document.

Reading /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-ave.fif ...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Left Auditory)
        0 CTF compensation matrices available
        nave = 55 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
No baseline correction applied
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Right Auditory)
        0 CTF compensation matrices available
        nave = 61 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
No baseline correction applied
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Left visual)
        0 CTF compensation matrices available
        nave = 67 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
No baseline correction applied
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Right visual)
        0 CTF compensation matrices available
        nave = 58 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
No baseline correction applied
Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
    366 x 366 full covariance (kind = 1) found.
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
Iterating over 1 potential files (this may take some
Rendering: /tmp/tmpkmim4pe7/sample_audvis-ave.fif
Reading /tmp/tmpkmim4pe7/sample_audvis-ave.fif ...
    Read a total of 1 projection items:
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Left Auditory)
        0 CTF compensation matrices available
        nave = 55 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
No baseline correction applied
Applying baseline correction (mode: mean)
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
Computing rank from covariance with rank=None
    Using tolerance 4.7e-14 (2.2e-16 eps * 59 dim * 3.6  max singular value)
    Estimated rank (eeg): 58
    EEG: rank 58 computed from 59 data channels with 1 projector
    Created an SSP operator (subspace dimension = 1)
Computing rank from covariance with rank={'eeg': 58}
    Setting small EEG eigenvalues to zero (without PCA)
    Created the whitener using a noise covariance matrix with rank 58 (1 small eigenvalues omitted)
Saving report to : /home/circleci/project/tutorials/intro/report_parse_folder_evoked.html

添加自定义 HTML（例如描述文本）#

add_html() 方法允许你向报告中添加自定义 HTML。这个功能非常方便，可以为报告添加简短描述、列表或提醒（以及你可以想到的许多其他可用 HTML 编码的内容）。

report = mne.Report(title="Report on hypothesis 1")

my_html = """
<p>We have the following hypothesis:</p>
<ol>
<li>There is a difference between images showing man-made vs. natural
environments</li>
<li>This difference manifests itself most strongly in the amplitude of the
N1 ERP component</li>
</ol>
<p>Below we show several plots and tests of the data.</p>
"""

report.add_html(title="Hypothesis", html=my_html)
report.save("report_add_html.html", overwrite=True)

The generated HTML document.

Embedding : jquery-3.6.0.min.js
Embedding : bootstrap.bundle.min.js
Embedding : bootstrap.min.css
Embedding : bootstrap-table/bootstrap-table.min.js
Embedding : bootstrap-table/bootstrap-table.min.css
Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js
Embedding : bootstrap-table/bootstrap-table-export.min.js
Embedding : bootstrap-table/tableExport.min.js
Embedding : bootstrap-icons/bootstrap-icons.mne.min.css
Embedding : highlightjs/highlight.min.js
Embedding : highlightjs/atom-one-dark-reasonable.min.css
Saving report to : /home/circleci/project/tutorials/intro/report_add_html.html

脚本总运行时间：（0 分钟 28.960 秒）

图库由 Sphinx-Gallery 生成

开始使用 mne.Report#

添加原始数据#

添加事件#

添加 Epochs#

添加 Evoked#

添加协方差#

添加投影向量#

添加 ICA#

添加带 BEM 的 MRI#

添加配准#

添加正演解#

添加 InverseOperator#

添加 SourceEstimate#

添加源代码（例如 Python 脚本）#

添加自定义图形#

添加图像文件#

使用标签#

编辑已保存的报告#

添加整个文件夹中的文件#

添加自定义 HTML（例如描述文本）#

开始使用 `mne.Report`#