Merge branch 'main' into dependabot/conda/sphinx-togglebutton-0.4.4

Author: mmcky

.github/workflows/cache.yml

@@ -23,13 +23,13 @@ jobs:
           jb build lectures --path-output ./ --keep-going
           # TODO: Re-enable -W flag once all lectures are translated and warnings are resolved
       - name: Upload Execution Reports
-        uses: actions/upload-artifact@v6
+        uses: actions/upload-artifact@v7
         if: failure()
         with:
           name: execution-reports
           path: _build/html/reports
       - name: Upload "_build" folder (cache)
-        uses: actions/upload-artifact@v6
+        uses: actions/upload-artifact@v7
         with:
           name: build-cache
           path: _build

.github/workflows/ci.yml

@@ -36,7 +36,7 @@ jobs:
         shell: bash -l {0}
         run: pip list
       - name: Download "build" folder (cache)
-        uses: dawidd6/action-download-artifact@v12
+        uses: dawidd6/action-download-artifact@v16
         with:
           workflow: cache.yml
           branch: main
@@ -62,7 +62,7 @@ jobs:
         run: |
           jb build lectures --path-output ./ -n --keep-going
       - name: Upload Execution Reports
-        uses: actions/upload-artifact@v6
+        uses: actions/upload-artifact@v7
         if: failure()
         with:
           name: execution-reports

.github/workflows/publish.yml

@@ -42,7 +42,7 @@ jobs:
         run: pip list
       # Download Build Cache from cache.yml
       - name: Download "build" folder (cache)
-        uses: dawidd6/action-download-artifact@v12
+        uses: dawidd6/action-download-artifact@v16
         with:
           workflow: cache.yml
           branch: main

environment.yml

@@ -7,7 +7,7 @@ dependencies:
   - pip
   - pip:
     - jupyter-book>=1.0.4post1,<2.0
-    - quantecon-book-theme==0.15.1
+    - quantecon-book-theme==0.18.0
     - sphinx-tojupyter==0.6.0
     - sphinxext-rediraffe==0.3.0
     - sphinx-exercise==1.2.1

lectures/_admonition/gpu.md

@@ -0,0 +1,12 @@
+```{admonition} GPU
+:class: warning
+
+This lecture was built using a machine with access to a GPU --- although it will also run without one.
+
+[Google Colab](https://colab.research.google.com/) has a free tier with GPUs
+that you can access as follows:
+
+1. Click on the "play" icon top right
+2. Select Colab
+3. Set the runtime environment to include a GPU
+```

lectures/_static/lecture_specific/need_for_speed/dgx.png

@@ -8,35 +8,38 @@ parts:
   - file: getting_started
   - file: python_by_example
   - file: functions
-  # - file: python_essentials
-  # - file: oop_intro
-  # - file: names
-  # - file: python_oop
-  # - file: workspace 
-# - caption: The Scientific Libraries
-#   numbered: true
-#   chapters:
-#   - file: need_for_speed
-#   - file: numpy
-#   - file: matplotlib
-#   - file: scipy
-#   - file: pandas
-#   - file: pandas_panel
-#   - file: sympy
-# - caption: High Performance Computing
-#   numbered: true
-#   chapters:
-#   - file: numba
-#   - file: parallelization
-#   - file: jax_intro
-# - caption: Advanced Python Programming
-#   numbered: true
-#   chapters:
-#   - file: writing_good_code
-#   - file: python_advanced_features
-#   - file: debugging
+  - file: python_essentials
+  - file: oop_intro
+  - file: names
+  - file: python_oop
+- caption: Foundations of Scientific Computing
+  numbered: true
+  chapters:
+  - file: need_for_speed
+  - file: numpy
+  - file: matplotlib
+  - file: scipy
+- caption: High Performance Computing
+  numbered: true
+  chapters:
+  - file: numba
+  - file: jax_intro
+  - file: numpy_vs_numba_vs_jax
+- caption: Working with Data
+  numbered: true
+  chapters:
+  - file: pandas
+  - file: pandas_panel
+- caption: More Python Programming
+  numbered: true
+  chapters:
+  - file: writing_good_code
+  - file: workspace 
+  - file: python_advanced_features
+  - file: debugging
+  - file: sympy
 - caption: Other
   numbered: true
   chapters:
-  # - file: troubleshooting
+  - file: troubleshooting
   - file: status

lectures/_static/lecture_specific/need_for_speed/geforce.png

@@ -263,6 +263,7 @@ b @ c
 
 درحالیکه هنوز سلطان پردازش آرایه ها در پایتون NumPy است، اما اکنون رقبای جدیدی نیز دارد؛ کتابخانه هایی مانند [CuPy](https://cupy.dev/)، [Pytorch](https://pytorch.org/)، [JAX](https://github.com/google/jax) نیز انواع آرایه ها و عملیات آرایه ای را تعبیه کرده اند و می توانند بسیار سریع و کارآمد باشند. همانطور که بعدا در این مجموعه توضیح خواهیم داد؛ در واقع این کتابخانه ها در استفاده از پردازش موازی و سخت افزار سریع، بهتر هستند. با این حال، شما هنوز هم باید ابتدا NumPy را یاد بگیرید، زیرا؛ ساده تر است و یک پایه ی قوی را فراهم می کند، به طور مستقیم عملکرد کتابخانه هایی مانند JAX را گسترش می دهد و از اینرو وقتی NumPy را می شناسید، یادگیری آن را نیز آسان تر می کند.
 
+(tuple_unpacking_example)=
 ### SciPy
 
 کتابخانه [SciPy](http://www.scipy.org) بر روی NumPy ساخته شده است و قابلیت های اضافی را ارائه می دهد.