MAINT: update anaconda=2025.06 and python=3.13

.github/workflows/cache.yml

@@ -1,8 +1,9 @@
 name: Build Cache [using jupyter-book]
 on:
-  push:
-    branches:
-      - main
+  schedule:
+    # Execute cache weekly at 3am on Monday
+    - cron:  '0 3 * * 1'
+  workflow_dispatch:
 jobs:
   tests:
     runs-on: ubuntu-latest
@@ -15,7 +16,7 @@ jobs:
           auto-update-conda: true
           auto-activate-base: true
           miniconda-version: 'latest'
-          python-version: "3.12"
+          python-version: "3.13"
           environment-file: environment.yml
           activate-environment: quantecon
       - name: graphviz Support # TODO: required?

.github/workflows/ci.yml

@@ -12,7 +12,7 @@ jobs:
           auto-update-conda: true
           auto-activate-base: true
           miniconda-version: 'latest'
-          python-version: "3.12"
+          python-version: "3.13"
           environment-file: environment.yml
           activate-environment: quantecon
       - name: Graphics Support #TODO: Review if graphviz is needed

.github/workflows/publish.yml

@@ -16,7 +16,7 @@ jobs:
           auto-update-conda: true
           auto-activate-base: true
           miniconda-version: 'latest'
-          python-version: "3.12"
+          python-version: "3.13"
           environment-file: environment.yml
           activate-environment: quantecon
       - name: Install latex dependencies

environment.yml

@@ -1,19 +1,19 @@
 name: quantecon
 channels:
   - default
-  - conda-forge
 dependencies:
-  - python=3.12
-  - anaconda=2024.10
+  - python=3.13
+  - anaconda=2025.06
   - pip
   - pip:
-    - jupyter-book==1.0.3
-    - quantecon-book-theme==0.8.2
-    - sphinx-tojupyter==0.3.0
+    - jupyter-book==1.0.4post1
+    - quantecon-book-theme==0.8.3
+    - sphinx-tojupyter==0.3.1
     - sphinxext-rediraffe==0.2.7
     - sphinx-exercise==1.0.1
-    - ghp-import==2.1.0
-    - sphinxcontrib-youtube==1.3.0 #Version 1.3.0 is required as quantecon-book-theme is only compatible with sphinx<=5
-    - sphinx-proof==0.2.0
+    - sphinx-proof==0.2.1
+    - sphinxcontrib-youtube==1.4.1
     - sphinx-togglebutton==0.3.2
-    - sphinx-reredirects==0.1.4 #Version 0.1.5 requires sphinx>=7.1
+    - sphinx-reredirects==1.0.0
+
+

lectures/eigen_I.md

@@ -997,12 +997,6 @@ Here is one solution.
 We start by looking into the distance between the eigenvector approximation and the true eigenvector.
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: Power iteration
-    name: pow-dist
----
 # Define a matrix A
 A = np.array([[1, 0, 3],
               [0, 2, 0],
@@ -1040,20 +1034,14 @@ print('The real eigenvalue is', np.linalg.eig(A)[0])
 plt.figure(figsize=(10, 6))
 plt.xlabel('iterations')
 plt.ylabel('error')
+plt.title('Power iteration')
 _ = plt.plot(errors)
 ```
 
-+++ {"user_expressions": []}
-
 Then we can look at the trajectory of the eigenvector approximation.
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: Power iteration trajectory
-    name: pow-trajectory
----
+
 # Set up the figure and axis for 3D plot
 fig = plt.figure()
 ax = fig.add_subplot(111, projection='3d')
@@ -1081,11 +1069,11 @@ ax.legend(points, ['actual eigenvector',
                    r'approximated eigenvector ($b_k$)'])
 ax.set_box_aspect(aspect=None, zoom=0.8)
 
+ax.set_title('Power iteration trajectory')
+
 plt.show()
 ```
 
-+++ {"user_expressions": []}
-
 ```{solution-end}
 ```
 
@@ -1119,21 +1107,14 @@ print(f'eigenvectors:\n {eigenvectors}')
 plot_series(A, v, n)
 ```
 
-+++ {"user_expressions": []}
-
 The result seems to converge to the eigenvector of $A$ with the largest eigenvalue.
 
 Let's use a [vector field](https://en.wikipedia.org/wiki/Vector_field) to visualize the transformation brought by A.
 
 (This is a more advanced topic in linear algebra, please step ahead if you are comfortable with the math.)
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: Convergence towards eigenvectors
-    name: eigen-conv
----
+
 # Create a grid of points
 x, y = np.meshgrid(np.linspace(-5, 5, 15),
                    np.linspace(-5, 5, 20))
@@ -1165,13 +1146,12 @@ plt.legend(lines, labels, loc='center left',
 
 plt.xlabel("x")
 plt.ylabel("y")
+plt.title("Convergence towards eigenvectors")
 plt.grid()
 plt.gca().set_aspect('equal', adjustable='box')
 plt.show()
 ```
 
-+++ {"user_expressions": []}
-
 Note that the vector field converges to the eigenvector of $A$ with the largest eigenvalue and diverges from the eigenvector of $A$ with the smallest eigenvalue.
 
 In fact, the eigenvectors are also the directions in which the matrix $A$ stretches or shrinks the space.
@@ -1200,13 +1180,8 @@ Use the visualization in the previous exercise to explain the trajectory of the
 Here is one solution
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: Vector fields of the three matrices
-    name: vector-field
----
-figure, ax = plt.subplots(1, 3, figsize=(15, 5))
+
+fig, ax = plt.subplots(1, 3, figsize=(15, 5))
 A = np.array([[sqrt(3) + 1, -2],
               [1, sqrt(3) - 1]])
 A = (1/(2*sqrt(2))) * A
@@ -1264,24 +1239,18 @@ for i, example in enumerate(examples):
     ax[i].grid()
     ax[i].set_aspect('equal', adjustable='box')
 
+fig.suptitle("Vector fields of the three matrices")
 plt.show()
 ```
 
-+++ {"user_expressions": []}
-
 The vector fields explain why we observed the trajectories of the vector $v$ multiplied by $A$ iteratively before.
 
 The pattern demonstrated here is because we have complex eigenvalues and eigenvectors.
 
 We can plot the complex plane for one of the matrices using `Arrow3D` class retrieved from [stackoverflow](https://stackoverflow.com/questions/22867620/putting-arrowheads-on-vectors-in-a-3d-plot).
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: 3D plot of the vector field
-    name: 3d-vector-field
----
+
 class Arrow3D(FancyArrowPatch):
     def __init__(self, xs, ys, zs, *args, **kwargs):
         super().__init__((0, 0), (0, 0), *args, **kwargs)
@@ -1334,11 +1303,10 @@ ax.set_ylabel('y')
 ax.set_zlabel('Im')
 ax.set_box_aspect(aspect=None, zoom=0.8)
 
+plt.title("3D plot of the vector field")
 plt.draw()
 plt.show()
 ```
 
-+++ {"user_expressions": []}
-
 ```{solution-end}
 ```