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docs(connectivity): update documentation with improved examples
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‎docs/usage/brain_connectivity.md‎

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# 脑连接图
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透明的大脑图，可以展示脑区间的连接关系。
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需要准备左右半脑的surface文件、脑区相关的nii.gz文件以及连接矩阵。
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```python
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import numpy as np
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from plotfig import *
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# 生成一个 31x31 的连接矩阵（对称加权矩阵，对角线为0）
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matrix = np.zeros((31, 31))
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matrix[0, 1] = 1
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matrix[0, 2] = 2
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matrix[0, 3] = 3
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matrix[4, 1] = -1
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matrix[4, 2] = -2
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matrix[4, 3] = -3
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matrix = (matrix + matrix.T) / 2 # 矩阵对称
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connectome = matrix
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output_file = "./figures/brain_connection.html"
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lh_surfgii_file = r"e:\6_Self\plot_self_brain_connectivity\103818.L.midthickness.32k_fs_LR.surf.gii"
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rh_surfgii_file = r"e:\6_Self\plot_self_brain_connectivity\103818.R.midthickness.32k_fs_LR.surf.gii"
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niigz_file = rf"e:\6_Self\plot_self_brain_connectivity\human_Self_processing_network.nii.gz"
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fig = plot_brain_connection_figure(
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connectome,
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lh_surfgii_file=lh_surfgii_file,
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rh_surfgii_file=rh_surfgii_file,
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niigz_file=niigz_file,
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output_file=output_file,
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scale_method="width",
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line_width=10,
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)
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```
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![png](brain_connectivity_files/human.gif)
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html文件可以在浏览器中交互。可以手动截图，也可以使用以下命令来生成图片。
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```python
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from pathlib import Path
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Path(f"./figures/brain_connection").mkdir(parents=True, exist_ok=True) # 新建文件夹保存帧图
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save_brain_connection_frames(fig, output_dir=rf"./figures/brain_connection", n_frames=36)
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```
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100%|██████████| 36/36 [02:01<00:00, 3.37s/it]
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保存了 36 张图片在 ./figures/brain_connection
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# 脑连接图
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透明的大脑图，可以展示脑区间的连接关系。
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需要准备左右半脑的surface文件、脑区相关的nii.gz文件以及连接矩阵。
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```python
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from plotfig import plot_brain_connection_figure, gen_symmetric_matrix
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# 生成随机的连接矩阵
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connectome = gen_symmetric_matrix(31, sparsity=0.1, seed=42)
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# 左右脑surface文件以及网络节点文件需自行提供
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lh_surfgii_file = r"example_data/103818.L.midthickness.32k_fs_LR.surf.gii"
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rh_surfgii_file = r"example_data/103818.R.midthickness.32k_fs_LR.surf.gii"
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niigz_file = r"example_data/network.nii.gz"
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# html文件输出位置
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output_file = "example_data/brain_connection.html"
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fig = plot_brain_connection_figure(
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connectome,
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lh_surfgii_file=lh_surfgii_file,
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rh_surfgii_file=rh_surfgii_file,
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niigz_file=niigz_file,
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output_file=output_file,
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scale_method="width",
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line_width=10,
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nodes_name=[f"ROI_{i}" for i in range(connectome.shape[0])],
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)
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```
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## 结果展示
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![output](brain_connectivity_files/output.gif)
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html文件可以在浏览器中交互。可以手动截图，也可以使用以下命令来批量生成多视角生成图片。
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```python
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from pathlib import Path
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from plotfig import save_brain_connection_frames
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# 新建文件夹保存帧图
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Path(f"./example_data/brain_connection_figures").mkdir(parents=True, exist_ok=True)
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save_brain_connection_frames(
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fig,
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output_dir=rf"./example_data/brain_connection_figures",
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n_frames=36
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)
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```
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100%|██████████| 36/36 [01:55<00:00, 3.19s/it]
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2025-11-24 11:02:55.867 | INFO | plotfig.brain_connection:save_brain_connection_frames:323 - 保存了 36 张图片在 ./example_data/brain_connection_figures
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plotfig 提供了将图片序列整合生成 GIF 动画的工具函数。
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```python
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from pathlib import Path
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from plotfig import create_gif_from_images
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create_gif_from_images(
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Path("example_data/brain_connection_figures"),
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output_name="output.gif"
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)
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```
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2025-11-24 11:07:46.885 | INFO | plotfig.brain_connection:create_gif_from_images:417 - GIF 已保存到: example_data\brain_connection_figures\outpug.gif
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‎docs/usage/brain_connectivity_files/human.gif‎

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‎docs/usage/brain_connectivity_files/output.gif‎

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‎notebooks/brain_connectivity.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from plotfig import *\n",
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"from plotfig import plot_brain_connection_figure, gen_symmetric_matrix\n",
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"\n",
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"# 生成一个 31x31 的连接矩阵（对称加权矩阵，对角线为0）\n",
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"matrix = np.zeros((31, 31))\n",
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"matrix[0, 1] = 1\n",
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"matrix[0, 2] = 2\n",
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"matrix[0, 3] = 3\n",
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"matrix[4, 1] = -1\n",
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"matrix[4, 2] = -2\n",
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"matrix[4, 3] = -3\n",
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"matrix = (matrix + matrix.T) / 2 # 矩阵对称\n",
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"\n",
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"connectome = matrix\n",
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"# 生成随机的连接矩阵\n",
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"connectome = gen_symmetric_matrix(31, sparsity=0.1, seed=42)\n",
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"\n",
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"output_file = \"./figures/brain_connection.html\"\n",
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"# 左右脑surface文件以及网络节点文件需自行提供\n",
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"lh_surfgii_file = r\"example_data/103818.L.midthickness.32k_fs_LR.surf.gii\"\n",
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"rh_surfgii_file = r\"example_data/103818.R.midthickness.32k_fs_LR.surf.gii\"\n",
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"niigz_file = r\"example_data/network.nii.gz\"\n",
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"\n",
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"lh_surfgii_file = r\"e:\\6_Self\\plot_self_brain_connectivity\\103818.L.midthickness.32k_fs_LR.surf.gii\"\n",
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"rh_surfgii_file = r\"e:\\6_Self\\plot_self_brain_connectivity\\103818.R.midthickness.32k_fs_LR.surf.gii\"\n",
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"niigz_file = rf\"e:\\6_Self\\plot_self_brain_connectivity\\human_Self_processing_network.nii.gz\"\n",
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"# html文件输出位置\n",
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"output_file = \"example_data/brain_connection.html\"\n",
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"\n",
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"fig = plot_brain_connection_figure(\n",
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" connectome,\n",
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" output_file=output_file,\n",
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" scale_method=\"width\",\n",
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" line_width=10,\n",
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")"
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" nodes_name=[f\"ROI_{i}\" for i in range(connectome.shape[0])],\n",
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")\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b2bd2fc8",
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"metadata": {},
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"source": [
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"## 结果展示"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b34d7c95",
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"metadata": {},
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"source": [
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"![output](brain_connectivity_files/output.gif)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3ffe6519",
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"metadata": {},
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"source": [
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"html文件可以在浏览器中交互。可以手动截图，也可以使用以下命令来生成图片。"
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"html文件可以在浏览器中交互。可以手动截图，也可以使用以下命令来批量生成多视角生成图片。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f257283e",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 36/36 [01:55<00:00, 3.19s/it]\n",
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"\u001b[32m2025-11-24 11:02:55.867\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mplotfig.brain_connection\u001b[0m:\u001b[36msave_brain_connection_frames\u001b[0m:\u001b[36m323\u001b[0m - \u001b[1m保存了 36 张图片在 ./example_data/brain_connection_figures\u001b[0m\n"
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]
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}
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],
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"source": [
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"from pathlib import Path\n",
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"from plotfig import save_brain_connection_frames\n",
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"\n",
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"\n",
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"Path(f\"./figures/brain_connection\").mkdir(parents=True, exist_ok=True) # 新建文件夹保存帧图\n",
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"save_brain_connection_frames(fig, output_dir=rf\"./figures/brain_connection\", n_frames=36)"
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"# 新建文件夹保存帧图\n",
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"Path(f\"./example_data/brain_connection_figures\").mkdir(parents=True, exist_ok=True)\n",
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"save_brain_connection_frames(\n",
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" fig,\n",
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" output_dir=rf\"./example_data/brain_connection_figures\",\n",
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" n_frames=36\n",
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")\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c1650800",
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"metadata": {},
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"source": [
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"plotfig 提供了将图片序列整合生成 GIF 动画的工具函数。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1012f97a",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[32m2025-11-24 11:07:46.885\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mplotfig.brain_connection\u001b[0m:\u001b[36mcreate_gif_from_images\u001b[0m:\u001b[36m417\u001b[0m - \u001b[1mGIF 已保存到: example_data\\brain_connection_figures\\outpug.gif\u001b[0m\n"
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]
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}
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],
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"source": [
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"from pathlib import Path\n",
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"from plotfig import create_gif_from_images\n",
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"\n",
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"create_gif_from_images(\n",
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" Path(\"example_data/brain_connection_figures\"),\n",
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" output_name=\"output.gif\"\n",
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")\n"
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]
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}
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],
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.11"
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}
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},

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