⬆️ Upgrade build software and dependencies (#293)

* ⬆️ Upgrade build software and dependencies

- Upgrade anaconda to 2025.12
- Upgrade quantecon-book-theme to 0.15.1
- Add jupyter-book version constraint (<2.0)
- Configure Dependabot for conda ecosystem with jupyter-book restriction
- Add nb_merge_streams and path_to_docs to _config.yml
- Temporarily disable build cache for full execution check

* Fix plotly orca timeout errors by using kaleido engine

- Add kaleido to environment.yml for reliable image export
- Update all fig.to_image() calls to use engine='kaleido'
- Fixes execution errors in BCG_complete_mkts, BCG_incomplete_mkts, and knowing_forecasts_of_others
- Kaleido is the modern replacement for orca and works reliably in CI environments

* Fix scipy gradient compatibility for Python 3.13

- Update objf_prime to return 1D gradient array instead of 2D
- Fixes 'Input array gradx must be 1D' error in amss2 and amss3
- Resolves compatibility issue with scipy.optimize.fmin_slsqp in Python 3.13

* ⬆️ Configure dependabot to ignore Python version updates

Python version should be constrained by the anaconda distribution version
specified in environment.yml, not updated independently by dependabot.

* Update .github/dependabot.yml

Co-authored-by: Copilot <[email protected]>

* Re-enable build cache in CI workflow

---------

Co-authored-by: Copilot <[email protected]>

Author: mmcky

.github/dependabot.yml

@@ -11,3 +11,16 @@ updates:
       prefix: ⬆️
     schedule:
       interval: weekly
+
+  - package-ecosystem: "conda"
+    directory: "/"
+    commit-message:
+      prefix: ⬆️
+    schedule:
+      interval: weekly
+    ignore:
+      - dependency-name: "jupyter-book"
+        versions: [">=2.0"]
+      - dependency-name: "python"
+        # Python version should be constrained by the anaconda distribution version
+        versions: [">0"]

environment.yml

@@ -3,17 +3,18 @@ channels:
   - default
 dependencies:
   - python=3.13
-  - anaconda=2025.06
+  - anaconda=2025.12
   - pip
   - pip:
-    - jupyter-book==1.0.4post1
-    - quantecon-book-theme==0.9.3
+    - jupyter-book>=1.0.4post1,<2.0
+    - quantecon-book-theme==0.15.1
     - sphinx-tojupyter==0.3.1
     - sphinxext-rediraffe==0.2.7
     - sphinx-exercise==1.0.1
     - sphinx-proof==0.2.1
     - sphinxcontrib-youtube==1.4.1
     - sphinx-togglebutton==0.3.2
     - sphinx-reredirects==0.1.4
+    - kaleido
 
 

lectures/BCG_complete_mkts.md

@@ -1194,7 +1194,7 @@ fig.update_layout(width=500,
 fig.update_layout(scene_camera=dict(eye=dict(x=2, y=-2, z=1.5)))
 
 # Export to PNG file
-Image(fig.to_image(format="png"))
+Image(fig.to_image(format="png", engine="kaleido"))
 # fig.show() will provide interactive plot when running
 # notebook locally
 ```

lectures/BCG_incomplete_mkts.md

@@ -1270,7 +1270,7 @@ fig.update_layout(scene_camera=dict(eye=dict(x=1.5, y=-1.5, z=2)))
 fig.update_layout(title='Equilibrium firm valuation for the grid of (k,b)')
 
 # Export to PNG file
-Image(fig.to_image(format="png"))
+Image(fig.to_image(format="png", engine="kaleido"))
 # fig.show() will provide interactive plot when running
 # code locally
 ```
@@ -1634,7 +1634,7 @@ fig.update_layout(title='Equilibrium firm valuation for the grid of (k,b)')
 
 
 # Export to PNG file
-Image(fig.to_image(format="png"))
+Image(fig.to_image(format="png", engine="kaleido"))
 # fig.show() will provide interactive plot when running
 # code locally
 ```
@@ -1685,7 +1685,7 @@ fig.update_layout(scene_camera=dict(eye=dict(x=1.5, y=-1.5, z=2)))
 fig.update_layout(title='Equilibrium firm valuation for the grid of (k,b)')
 
 # Export to PNG file
-Image(fig.to_image(format="png"))
+Image(fig.to_image(format="png", engine="kaleido"))
 # fig.show() will provide interactive plot when running
 # code locally
 ```
@@ -1746,7 +1746,7 @@ fig.update_layout(title='Equilibrium equity valuation for the grid of (k,b)')
 
 
 # Export to PNG file
-Image(fig.to_image(format="png"))
+Image(fig.to_image(format="png", engine="kaleido"))
 # fig.show() will provide interactive plot when running
 # code locally
 ```
@@ -1776,7 +1776,7 @@ fig.update_layout(title='Equilibrium bond valuation for the grid of (k,b)')
 
 
 # Export to PNG file
-Image(fig.to_image(format="png"))
+Image(fig.to_image(format="png", engine="kaleido"))
 # fig.show() will provide interactive plot when running
 # code locally
 ```

lectures/_config.yml

@@ -39,6 +39,7 @@ sphinx:
                        'https://doi.org/10.1086/262078',
                        'https://keras.io/',
                        'https://data.oecd.org/']
+    nb_merge_streams: true
     nb_mime_priority_overrides: [
        # HTML
        ['html', 'application/vnd.jupyter.widget-view+json', 10],
@@ -85,6 +86,7 @@ sphinx:
       header_organisation: QuantEcon
       repository_url: https://github.com/QuantEcon/lecture-python-advanced.myst
       nb_repository_url: https://github.com/QuantEcon/lecture-python-advanced.notebooks
+      path_to_docs: lectures
       twitter: quantecon
       twitter_logo_url: https://assets.quantecon.org/img/qe-twitter-logo.png
       og_logo_url: https://assets.quantecon.org/img/qe-og-logo.png

lectures/_static/lecture_specific/amss2/recursive_allocation.py

@@ -231,15 +231,15 @@ def objf_prime(x):
 
             epsilon = 1e-7
             x0 = np.asarray(x, dtype=float)
-            f0 = np.atleast_1d(objf(x0))
-            jac = np.zeros([len(x0), len(f0)])
+            f0 = objf(x0)
+            grad = np.zeros(len(x0))
             dx = np.zeros(len(x0))
             for i in range(len(x0)):
                 dx[i] = epsilon
-                jac[i] = (objf(x0+dx) - f0)/epsilon
+                grad[i] = (objf(x0+dx) - f0)/epsilon
                 dx[i] = 0.0
 
-            return jac.transpose()
+            return grad
 
         def cons(z):
             c, n, xprime, T = z[:S], z[S:2 * S], z[2 * S:3 * S], z[3 * S:]

lectures/knowing_forecasts_of_others.md

@@ -1020,7 +1020,7 @@ fig.update_layout(title=r'Impulse Response Function',
                    yaxis_title=r'$k^{i}_{t}$')
 fig1 = fig
 # Export to PNG file
-Image(fig1.to_image(format="png"))
+Image(fig1.to_image(format="png", engine="kaleido"))
 # fig1.show() will provide interactive plot when running
 # notebook locally
 ```