Fix cloud deployment issues

.github/workflows/deploy.yml

@@ -62,11 +62,18 @@ on:
   workflow_dispatch:
 
 permissions:
-  contents: write
+  contents: read
+  pages: write
+  id-token: write
+
+# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
+# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
 
 jobs:
-  deploy:
-    # available images: https://github.com/actions/runner-images#available-images
+  build:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout 🛎️
@@ -80,7 +87,10 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: "3.13"
-          cache: "pip" # caching pip dependencies
+          cache: "pip"
+      - name: Setup Pages
+        id: pages
+        uses: actions/configure-pages@v4
       - name: Update _config.yml ⚙️
         uses: fjogeleit/yaml-update-action@main
         with:
@@ -93,13 +103,26 @@ jobs:
           sudo apt-get update && sudo apt-get install -y imagemagick
           pip3 install --upgrade nbconvert
           export JEKYLL_ENV=production
-          bundle exec jekyll build
+          bundle exec jekyll build --baseurl "${{ steps.pages.outputs.base_path }}"
+        env:
+          JEKYLL_ENV: production
       - name: Purge unused CSS 🧹
         run: |
           npm install -g purgecss
           purgecss -c purgecss.config.js
-      - name: Deploy 🚀
-        if: github.event_name != 'pull_request'
-        uses: JamesIves/github-pages-deploy-action@v4
+      - name: Upload artifact 📦
+        uses: actions/upload-pages-artifact@v3
         with:
-          folder: _site
+          path: _site
+
+  deploy:
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    needs: build
+    if: github.event_name != 'pull_request'
+    steps:
+      - name: Deploy to GitHub Pages 🚀
+        id: deployment
+        uses: actions/deploy-pages@v4

.prettierignore

@@ -9,3 +9,4 @@ assets/plotly/demo.html
 lighthouse_results/**
 _posts/2015-10-20-math.md
 _sass/font-awesome/*.scss
+_scripts/**/*.js

README.md

@@ -13,7 +13,7 @@ This website is built using the [al-folio](https://github.com/alshedivat/al-foli
 ## Features
 
 - **Research Projects**: Showcase of current and past research work
-- **Blog**: Technical blog posts and research updates  
+- **Blog**: Technical blog posts and research updates
 - **Publications**: (Coming soon) Auto-generated from BibTeX
 - **CV**: Online curriculum vitae
 - **Light/Dark Mode**: Theme switcher for comfortable viewing

_config.yml

@@ -17,7 +17,7 @@ lang: en # the language of your site (for example: en, fr, cn, ru, etc.)
 icon: lemon.ico # the emoji used as the favicon (alternatively, provide image name in /assets/img/)
 
 url: https://arnold117.github.io # the base hostname & protocol for your site
-baseurl:  # the subpath of your site, e.g. /blog/. Leave blank for root
+baseurl: # the subpath of your site, e.g. /blog/. Leave blank for root
 last_updated: false # set to true if you want to display last updated in the footer
 impressum_path: # set to path to include impressum link in the footer, use the same path as permalink in a page, helps to conform with EU GDPR
 back_to_top: true # set to false to disable the back to top button

_pages/about.md

@@ -23,13 +23,15 @@ Hi! I'm (Arnold) Yanuo Zhou (周亚诺), a researcher focused on applying machin
 ## Research Interests
 
 My research sits at the intersection of:
+
 - **Machine Learning for Digital Phenotyping**: Developing computational methods to characterize health and behavior from digital data
 - **Wearable Sensing**: Leveraging wearable devices to capture continuous physiological and behavioral signals
 - **Light & Health**: Understanding how light exposure patterns impact human well-being and circadian rhythms
 
 ## Current Work
 
 I'm currently working on projects that use machine learning to:
+
 - Analyze light exposure patterns from wearable sensors
 - Develop predictive models for health outcomes based on digital phenotypes
 - Create tools for personalized health monitoring and intervention

_pages/blog.md

@@ -30,12 +30,13 @@ pagination:
   {% endif %}
 
 {% comment %}
-  Show up to the top 4 tags by post count. If there are fewer than 4 tags, show all.
-  This picks the highest-count tags iteratively without requiring external filters.
+Show up to the top 4 tags by post count. If there are fewer than 4 tags, show all.
+This picks the highest-count tags iteratively without requiring external filters.
 {% endcomment %}
 {% assign all_tags = site.tags %}
 {% assign chosen = "" | split: "" %}
 {% if all_tags and all_tags.size > 0 or site.display_categories and site.display_categories.size > 0 %}
+
   <div class="tag-category-list">
     <ul class="p-0 m-0">
       {% comment %} pick up to 4 tags with largest post counts {% endcomment %}
@@ -87,6 +88,7 @@ pagination:
     <div style="margin-top:0.5rem">
       <a href="{{ '/tags/' | relative_url }}">View all tags</a>
     </div>
+
   </div>
 {% endif %}
 

_pages/tags.md

@@ -19,4 +19,4 @@ nav_order: 2
       </div>
     {% endfor %}
   </div>
-</div>
+</div>

_posts/2021-04-29-vmware_ssh.md

@@ -1,7 +1,7 @@
 ---
 layout: post
 title: "Windows用SSH连接虚拟机Linux"
-date:   2021-04-29
+date: 2021-04-29
 tags: [Linux]
 giscus_comments: true
 author: Arnold
@@ -13,15 +13,20 @@ author: Arnold
 虚拟机除了性能差点，其余可谓是和实体机没什么差别，但直接在虚拟机内操作可能体验感比较差，要不，我们用ssh远程连接虚拟机的终端吧！
 
 <!-- more -->
+
 ## 1. 准备工作
+
 详细的安装步骤略。我使用的是VMware Workstation Pro 16.0安装了Ubuntu 20.04LST  
 默认虚拟机的网络适配器是`NAT`模式，因为学校的校园网登录需要认证，我就让它保持不变。  
-然后就是要知道宿主机和虚拟机的ip地址  
+然后就是要知道宿主机和虚拟机的ip地址
+
 ### 宿主机 Windows 10
+
 打开终端，我使用的终端是 `Windows Terminal`中的CMD。  
 在终端中输入: `ipconfig`  
-你会得到很多输出，比如，我现在使用的是无线网络，宿主机ip就在`无线局域网适配器 WLAN` 寻找:  
-``` bash
+你会得到很多输出，比如，我现在使用的是无线网络，宿主机ip就在`无线局域网适配器 WLAN` 寻找:
+
+```bash
 无线局域网适配器 WLAN:
 
    连接特定的 DNS 后缀 . . . . . . . : pc.*.edu.cn
@@ -30,13 +35,17 @@ author: Arnold
    子网掩码  . . . . . . . . . . . . : 255.*.*.*
    默认网关. . . . . . . . . . . . . : 10.*.*.*
 ```
-其中关键信息我手动用了*隐藏，宿主机的ip就是IPv4地址，即 `10.*.*.*`  
+
+其中关键信息我手动用了*隐藏，宿主机的ip就是IPv4地址，即 `10.*._._`
+
 ### 虚拟机 Ubuntu 20.04
+
 打开终端，输入命令: `ifconfig`  
 如果如图提示 `ifconfig not found`
 ![ifconfig_not_found](/assets/img/post/2021/04/29/vmware_ssh/Ubuntu_terminal_ifconfig_not_found.png){: style="max-width: 100%; height: auto;" }  
 就`sudo apt install net-tools`就好了，可以再次执行命令  
 现在输出的是：
+
 ```bash
 ens33: flags=4163<UP,BROADCAST,RUNNING,MULTICAST>  mtu 1500
         inet 192.*.*.*1  netmask 255.*.*.*  broadcast 192.*.*.*0
@@ -56,26 +65,32 @@ lo:    flags=73<UP,LOOPBACK,RUNNING>  mtu 65536
         TX packets 519  bytes 46328 (46.3 KB)
         TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0
 ```
-虚拟机的ip地址就是`ens33:inet`地址，即`192.*.*.*1`  
+
+虚拟机的ip地址就是`ens33:inet`地址，即`192.*.*.*1`
+
 ## 2. 建立IP映射
-然后，我们需要将主机与宿主机的ip映射到一起。  
+
+然后，我们需要将主机与宿主机的ip映射到一起。
 
 打开 编辑->虚拟网络编辑器，如图所示：  
 ![Edit_menu](/assets/img/post/2021/04/29/vmware_ssh/Edit_menu.png){: style="max-width: 100%; height: auto;" }  
 然后选择**NAT模式**的连接，进入**NAT设置(S)**, 再点击**添加(A)**, 如图：  
 ![Virtual_network_editor](/assets/img/post/2021/04/29/vmware_ssh/Virtual_network_editor.png){: style="max-width: 100%; height: auto;" }  
-弹出的**映射传入端口**中:  
+弹出的**映射传入端口**中:
+
 - 主机端口，默认是22，
 - 虚拟机ip地址，填写上面找到的虚拟机ip
 - 虚拟机端口，默认22即可
 
 通过上述步骤，我们就成功将 `{主机ip}:22` 与 `{虚拟机ip}:22` 映射到了一起。
+
 ## 3. 配置虚拟机SSH
+
 - 安装openssh-client：`sudo apt install openssh-client`
 - 安装openssh-server：`sudo apt install openssh-server`
 - 启动ssh-server：`sudo /etc/init.d/ssh start`
 - 确认ssh-server工作正常：`netstat -tpl`  
-如果你看到了有`ssh`的字样，就说明ssh正在运行
+  如果你看到了有`ssh`的字样，就说明ssh正在运行
 
 至此，成功安装ssh，并且可以通过ssh工具在宿主机上使用。
 
@@ -84,12 +99,12 @@ lo:    flags=73<UP,LOOPBACK,RUNNING>  mtu 65536
 比如，我使用`Windows Terminal`中的ssh工具  
 直接在命令行输入:
 `ssh {虚拟机用户名}@{虚拟机ip}`，然后坚持要访问  
-等密码提示框出现时输入密码，就可以愉快远程访问啦！  
+等密码提示框出现时输入密码，就可以愉快远程访问啦！
 
 我们来执行一个很基础的命令：  
 在控制台输出 `Hello World!`:
-![Echo_hello_world](/assets/img/post/2021/04/29/vmware_ssh/Echo_hello_world.png){: style="max-width: 100%; height: auto;" }  
+![Echo_hello_world](/assets/img/post/2021/04/29/vmware_ssh/Echo_hello_world.png){: style="max-width: 100%; height: auto;" }
 
-此文就到此结束啦！欢迎大家在评论区留言哦ヾ(^▽^*)))  
+此文就到此结束啦！欢迎大家在评论区留言哦ヾ(^▽^\*)))  
 Ciallo～(∠・ω< )⌒☆​  
-写文不易，如果你觉得我的文章对你有帮助，欢迎[打赏](https://arnold117.github.io/likes/)！
+写文不易，如果你觉得我的文章对你有帮助，欢迎[打赏](https://arnold117.github.io/likes/)！

_posts/2021-06-19-python_plots.md

@@ -1,7 +1,7 @@
 ---
 layout: post
 title: "简单好用的Python论文绘图专用工具包-SciencePlots"
-date:   2021-06-19
+date: 2021-06-19
 tags: [Python]
 giscus_comments: true
 author: Arnold
@@ -16,7 +16,7 @@ author: Arnold
 
 SciencePlots是一个专门为科研论文打造的轻量化的绘图工具包，安装SciencePlots最简单的方式是使用pip，使用的指令为：
 
-``` bash
+```bash
 # 安装最新版
 pip install git+https://github.com/garrettj403/SciencePlots.git
 
@@ -28,13 +28,16 @@ pip install SciencePlots
 
 安装完成后，使用SciencePlots也非常简单，只需要导入matplotlib工具包，选择相应的style即可。  
 例如，如果想要给Science投稿，那么只需要引入下列的主题：
-``` python
+
+```python
 import matplotlib.pyplot as plt
 
 plt.style.use('science')
 ```
+
 如果想使用IEEE格式，则只需要：
-``` python
+
+```python
 import matplotlib.pyplot as plt
 
 plt.style.use('science', 'ieee')
@@ -44,18 +47,19 @@ plt.style.use('science', 'ieee')
 
 接下来，用一些例子来展示一下SciencePlots的用法和具体的效果，首先，引入一些初始的数据并进行绘制。
 
-``` python
+```python
 import numpy as np
 import matplotlib.pyplot as plt
 
-def model(x, p): 
+def model(x, p):
    return x ** (2 * p + 1) / (1 + x ** (2 * p))
 
 x = np.linspace(0.75, 1.25, 201)
 ```
 
 如果不使用SciencePlots, 使用Matplotlib：
-``` python
+
+```python
 fig, ax = plt.subplots()
 
 for p in [10, 15, 20, 30, 50, 100]:
@@ -67,6 +71,7 @@ ax.set(ylabel='Current ($\mu$A)')
 ax.autoscale(tight=True)
 fig.savefig('./fig1.jpg', dpi=300)
 ```
+
 则产生的图片是这样的：
 ![](/assets/img/post/2021/06/20/fig.jpg){: style="max-width: 100%; height: auto;" }  
 是不是很，，对吧。
@@ -75,7 +80,8 @@ fig.savefig('./fig1.jpg', dpi=300)
 ![](/assets/img/post/2021/06/20/fig1.jpg){: style="max-width: 100%; height: auto;" }  
 无论是坐标轴的刻度线，字体都变得高大上了很多。  
 具体是这样实现的：
-``` python
+
+```python
 with plt.style.context(['science']):
     fig, ax = plt.subplots()
     for p in [10, 15, 20, 30, 50, 100]:
@@ -87,9 +93,8 @@ with plt.style.context(['science']):
     fig.savefig('figures/fig1.jpg', dpi=300)
 ```
 
-
 想要了解更多有关SciencePlots的内容，可以去SciencePlots的官方Github了解更多。[Github链接](https://github.com/garrettj403/SciencePlots)
 
-此文就到此结束啦！欢迎大家在评论区留言哦ヾ(^▽^*)))  
+此文就到此结束啦！欢迎大家在评论区留言哦ヾ(^▽^\*)))  
 Ciallo～(∠・ω< )⌒☆​  
-写文不易，如果你觉得我的文章对你有帮助，欢迎[打赏](https://arnold117.github.io/likes/)！
+写文不易，如果你觉得我的文章对你有帮助，欢迎[打赏](https://arnold117.github.io/likes/)！