MAINT: upgrade anaconda=2025.06 and python=3.13

.github/workflows/cache.yml

@@ -15,7 +15,7 @@ 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

.github/workflows/ci.yml

@@ -14,7 +14,7 @@ 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
@@ -36,13 +36,13 @@ jobs:
       - name: Display Pip Versions
         shell: bash -l {0}
         run: pip list
-      - name: Download "build" folder (cache)
-        uses: dawidd6/action-download-artifact@v11
-        with:
-          workflow: cache.yml
-          branch: main
-          name: build-cache
-          path: _build
+      # - name: Download "build" folder (cache)
+      #   uses: dawidd6/action-download-artifact@v11
+      #   with:
+      #     workflow: cache.yml
+      #     branch: main
+      #     name: build-cache
+      #     path: _build
       # Build Assets (Download Notebooks and PDF via LaTeX)
       - name: Build PDF from LaTeX
         shell: bash -l {0}

.github/workflows/execution.yml

@@ -11,7 +11,7 @@ jobs:
       fail-fast: false
       matrix:
         os: ["ubuntu-latest"]
-        python-version: ["3.12"]
+        python-version: ["3.13"]
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -53,7 +53,7 @@ jobs:
       fail-fast: false
       matrix:
         os: ["macos-latest"]
-        python-version: ["3.12"]
+        python-version: ["3.13"]
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -85,7 +85,7 @@ jobs:
   #     fail-fast: false
   #     matrix:
   #       os: ["windows-latest"]
-  #       python-version: ["3.11"]
+  #       python-version: ["3.13"]
   #   steps:
   #     - name: Checkout
   #       uses: actions/checkout@v2

.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

.gitignore

@@ -1,2 +1,4 @@
 _build/
-.DS_Store
+.DS_Store
+.ipynb_checkpoints/
+.virtual_documents/

environment.yml

@@ -2,20 +2,18 @@ name: quantecon
 channels:
   - default
 dependencies:
-  - python=3.12
-  - anaconda=2024.10
+  - python=3.13
+  - anaconda=2025.06
   - pip
   - pip:
-    - jupyter-book==1.0.3
-    - quantecon-book-theme==0.7.6
-    - 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-reredirects==0.1.4
     - sphinx-exercise==1.0.1
-    - sphinx-proof==0.2.0
-    - ghp-import==1.1.0
-    - sphinxcontrib-youtube==1.3.0 #Version 1.3.0 is required as quantecon-book-theme is only compatible with sphinx<=5
+    - sphinx-proof==0.2.1
+    - sphinxcontrib-youtube==1.4.1
     - sphinx-togglebutton==0.3.2
-    # Docker Requirements
-    - pytz
+    - sphinx-reredirects==0.1.4
+
 

lectures/_static/lecture_specific/amss2/recursive_allocation.py

@@ -230,7 +230,7 @@ def objf(z):
         def objf_prime(x):
 
             epsilon = 1e-7
-            x0 = np.asfarray(x)
+            x0 = np.asarray(x, dtype=float)
             f0 = np.atleast_1d(objf(x0))
             jac = np.zeros([len(x0), len(f0)])
             dx = np.zeros(len(x0))

lectures/match_transport.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.17.2
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -13,13 +13,11 @@ kernelspec:
 
 # Composite Sorting
 
-+++
-
 ## Overview 
 
 Optimal transport theory studies how a marginal probabilty measure  can  be related to another marginal probability measure in an ideal way.
 
-  * here ideal means to minimize some  cost criterion. 
+* here ideal means to minimize some  cost criterion. 
 
 The output of such a theory is a **coupling** of the two probability measures, i.e., a joint probabilty
 measure having those two  marginal probability measures.  
@@ -575,9 +573,9 @@ def plot_H_z(self, figsize=(15, 8), range_x_axis=None, scatter=True):
     plt.axhline(0, color='black', linewidth=1)
 
     # determine the step points for horizontal lines
-    step = np.concatenate(([self.support_z.min() - .05 * self.support_z.ptp()],
+    step = np.concatenate(([self.support_z.min() - .05 * np.ptp(self.support_z)],
                             self.support_z,
-                           [self.support_z.max() + .05 * self.support_z.ptp()]))
+                           [self.support_z.max() + .05 * np.ptp(self.support_z)]))
     height = np.concatenate(([0], H_z, [0]))
 
     # plot the horizontal lines of the step function
@@ -699,9 +697,9 @@ def plot_layers(self, figsize=(15, 8)):
     plt.figure(figsize=figsize)
 
     # Plot H(z)
-    step = np.concatenate(([self.support_z.min() - .05 * self.support_z.ptp()],
+    step = np.concatenate(([self.support_z.min() - .05 * np.ptp(self.support_z)],
                            self.support_z,
-                           [self.support_z.max() + .05 * self.support_z.ptp()]))
+                           [self.support_z.max() + .05 * np.ptp(self.support_z)]))
     height = np.concatenate((H_z, [0]))
     plt.step(step, height, where='post', color='black', label='CDF', zorder=1)
 
@@ -984,7 +982,7 @@ def plot_layer_matching(self, layer, matching_layer):
     ax.spines['top'].set_color('none') 
     ax.spines['right'].set_color('none')  
     ax.yaxis.set_ticks([])
-    ax.set_ylim(bottom= -self.support_z.ptp() / 100)
+    ax.set_ylim(bottom= -np.ptp(self.support_z) / 100)
 
     plt.show()
 
@@ -1319,29 +1317,29 @@ def plot_matching(self, matching_off_diag, title, figsize=(15, 15),
     ax.spines['top'].set_color('none')
     ax.spines['right'].set_color('none')
     ax.yaxis.set_ticks([])
-    ax.set_ylim(- self.X_types.ptp() / 10, 
-                (max_height / 2) + self.X_types.ptp()*.01)  
+    ax.set_ylim(- np.ptp(self.X_types) / 10, 
+                (max_height / 2) + np.ptp(self.X_types)*.01)  
 
     # Plot H_z on the main axis if enabled
     if plot_H_z:
         H_z = np.cumsum(self.q_z)  
 
         step = np.concatenate(([self.support_z.min() 
-                                - .02 * self.support_z.ptp()],
+                                - .02 * np.ptp(self.support_z)],
                                 self.support_z,
                                 [self.support_z.max() 
-                                + .02 * self.support_z.ptp()]))
+                                + .02 * np.ptp(self.support_z)]))
 
-        H_z = H_z/H_z.ptp() * self.support_z.ptp() /2
+        H_z = H_z/np.ptp(H_z) * np.ptp(self.support_z) /2
         height = np.concatenate(([0], H_z, [0]))
 
         # Plot the compressed H_z on the same main x-axis
         ax.step(step, height, color='green', lw=2, 
                                 label='$H_z$', where='post')
 
         # Set the y-limit to keep H_z and maximum circle size in the plot
-        ax.set_ylim(np.min(H_z) - H_z.ptp() *.01,
-                    np.maximum(np.max(H_z), max_height / 2) + H_z.ptp() *.01) 
+        ax.set_ylim(np.min(H_z) - np.ptp(H_z) *.01,
+                    np.maximum(np.max(H_z), max_height / 2) + np.ptp(H_z) *.01) 
 
         # Add label and legend for H_z
         ax.legend(loc="upper right")
@@ -1614,7 +1612,7 @@ example_2.plot_matching(matching_NAM, title = 'NAM',
 
 +++ {"user_expressions": []}
 
-### Example 3 
+### Example 3
 
 +++ {"user_expressions": []}
 
@@ -1907,7 +1905,7 @@ def plot_hierarchies(self, subpairs, scatter=True, range_x_axis=None):
 
     if range_x_axis is not None:
         ax.set_xlim(range_x_axis)
-        ax.set_ylim(- self.X_types.ptp() / 10, 
+        ax.set_ylim(- np.ptp(self.X_types) / 10, 
                     (range_x_axis[1] - range_x_axis[0]) / 2 )
 
     # Title and layout settings for the main plot
@@ -2230,12 +2228,11 @@ We plot the wage standard deviation for the sorted occupations.
 ---
 mystnb:
   figure:
-    caption: "Average wage for each Standard Occupational Classification (SOC) code.
-    The codes are sorted by average wage on the horizontal axis. In red,
-    a polynomial of degree 5 is fitted to the data. The size of the marker is 
-    proportional to the number of individuals in the occupation."
+    caption: Average wage for each Standard Occupational Classification (SOC) code.
+      The codes are sorted by average wage on the horizontal axis. In red, a polynomial
+      of degree 5 is fitted to the data. The size of the marker is proportional to
+      the number of individuals in the occupation.
 ---
-
 # Scatter plot wage dispersion for each occupation
 plt.figure(figsize=(10, 6))
 
@@ -2273,11 +2270,10 @@ We also plot the average wages for each occupation (SOC code). Again, occupation
 ---
 mystnb:
   figure:
-    caption: "Average wage for each Standard Occupational Classification (SOC) code.
-    The codes are sorted by average wage on the horizontal axis. In red,
-    a polynomial of degree 5 is fitted to the data."
+    caption: Average wage for each Standard Occupational Classification (SOC) code.
+      The codes are sorted by average wage on the horizontal axis. In red, a polynomial
+      of degree 5 is fitted to the data.
 ---
-
 # Scatter plot average wage for each occupation
 plt.figure(figsize=(10, 6))
 

lectures/stationary_densities.md

@@ -784,8 +784,8 @@ In doing so, set $\theta = 0.8$ and $n = 500$.
 
 The next figure shows the result of such a computation
 
-```{figure} /_static/lecture_specific/stationary_densities/solution_statd_ex1.png
-
+```{image} /_static/lecture_specific/stationary_densities/solution_statd_ex1.png
+:align: center
 ```
 
 The additional density (black line) is a [nonparametric kernel density estimate](https://en.wikipedia.org/wiki/Kernel_density_estimation), added to the solution for illustration.