MAINT: upgrade anaconda=2025.06 and python=3.13

.github/workflows/cache.yml

@@ -17,7 +17,7 @@ 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 JAX, Numpyro, PyTorch

.github/workflows/ci.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 JAX, Numpyro, PyTorch

.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 JAX, Numpyro, PyTorch

environment.yml

@@ -2,17 +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
+    - sphinx-reredirects==0.1.4
+
+

lectures/career.md

@@ -370,7 +370,8 @@ when the worker follows the optimal policy.
 
 In particular, modulo randomness, reproduce the following figure (where the horizontal axis represents time)
 
-```{figure} /_static/lecture_specific/career/career_solutions_ex1_py.png
+```{image} /_static/lecture_specific/career/career_solutions_ex1_py.png
+:align: center
 ```
 
 ```{hint}

lectures/finite_markov.md

@@ -1113,7 +1113,8 @@ is known as [PageRank](https://en.wikipedia.org/wiki/PageRank).
 
 To illustrate the idea, consider the following diagram
 
-```{figure} /_static/lecture_specific/finite_markov/web_graph.png
+```{image} /_static/lecture_specific/finite_markov/web_graph.png
+:align: center
 ```
 
 Imagine that this is a miniature version of the WWW, with

lectures/ifp.md

@@ -548,7 +548,8 @@ Let's consider how the interest rate affects consumption.
 
 Reproduce the following figure, which shows (approximately) optimal consumption policies for different interest rates
 
-```{figure} /_static/lecture_specific/ifp/ifp_policies.png
+```{image} /_static/lecture_specific/ifp/ifp_policies.png
+:align: center
 ```
 
 * Other than `r`, all parameters are at their default values.

lectures/kalman.md

@@ -565,7 +565,8 @@ In the simulation, take $\theta = 10$, $\hat x_0 = 8$ and $\Sigma_0 = 1$.
 
 Your figure should -- modulo randomness -- look something like this
 
-```{figure} /_static/lecture_specific/kalman/kl_ex1_fig.png
+```{image} /_static/lecture_specific/kalman/kl_ex1_fig.png
+:align: center
 ```
 
 ```{exercise-end}
@@ -629,7 +630,8 @@ Plot $z_t$ against $T$, setting $\epsilon = 0.1$ and $T = 600$.
 
 Your figure should show error erratically declining something like this
 
-```{figure} /_static/lecture_specific/kalman/kl_ex2_fig.png
+```{image} /_static/lecture_specific/kalman/kl_ex2_fig.png
+:align: center
 ```
 
 ```{exercise-end}
@@ -732,7 +734,8 @@ Finally, set $x_0 = (0, 0)$.
 
 You should end up with a figure similar to the following (modulo randomness)
 
-```{figure} /_static/lecture_specific/kalman/kalman_ex3.png
+```{image} /_static/lecture_specific/kalman/kalman_ex3.png
+:align: center
 ```
 
 Observe how, after an initial learning period, the Kalman filter performs quite well, even relative to the competitor who predicts optimally with knowledge of the latent state.

lectures/likelihood_bayes.md

@@ -606,7 +606,7 @@ A correct Bayesian approach should directly model the uncertainty about $x$ and
 
 Here is the algorithm:
 
-First we specify a prior distribution for $x$ given by $x \sim \text{Beta}(\alpha_0, \beta_0)$ with sexpectation $\mathbb{E}[x] = \frac{\alpha_0}{\alpha_0 + \beta_0}$.
+First we specify a prior distribution for $x$ given by $x \sim \text{Beta}(\alpha_0, \beta_0)$ with expectation $\mathbb{E}[x] = \frac{\alpha_0}{\alpha_0 + \beta_0}$.
 
 The likelihood for a single observation $w_t$ is $p(w_t|x) = x f(w_t) + (1-x) g(w_t)$. 
 

lectures/markov_perf.md

@@ -722,7 +722,8 @@ c1 = c2 = np.array([1, -2, 1])
 e1 = e2 = np.array([10, 10, 3])
 ```
 
-```{figure} /_static/lecture_specific/markov_perf/judd_fig2.png
+```{image} /_static/lecture_specific/markov_perf/judd_fig2.png
+:align: center
 ```
 
 Inventories trend to a common steady state.
@@ -731,7 +732,8 @@ If we increase the depreciation rate to $\delta = 0.05$, then we expect steady s
 
 This is indeed the case, as the next figure shows
 
-```{figure} /_static/lecture_specific/markov_perf/judd_fig1.png
+```{image} /_static/lecture_specific/markov_perf/judd_fig1.png
+:align: center
 ```
 
 In this exercise, reproduce the figure when $\delta = 0.02$.

lectures/ols.md

@@ -604,7 +604,7 @@ results.
 
 ```{code-cell} python3
 # Load in data
-df4 = pd.read_stata('https://github.com/QuantEcon/lecture-python/blob/master/source/_static/lecture_specific/ols/maketable4.dta?raw=true')
+df4 = pd.read_stata('https://github.com/QuantEcon/lecture-python.myst/raw/refs/heads/main/lectures/_static/lecture_specific/ols/maketable4.dta')
 
 # Add a constant term
 df4['const'] = 1
@@ -677,7 +677,7 @@ using `numpy` - your results should be the same as those in the
 
 ```{code-cell} python3
 # Load in data
-df1 = pd.read_stata('https://github.com/QuantEcon/lecture-python/blob/master/source/_static/lecture_specific/ols/maketable1.dta?raw=true')
+df1 = pd.read_stata('https://github.com/QuantEcon/lecture-python.myst/raw/refs/heads/main/lectures/_static/lecture_specific/ols/maketable1.dta')
 df1 = df1.dropna(subset=['logpgp95', 'avexpr'])
 
 # Add a constant term

lectures/optgrowth_fast.md

@@ -344,7 +344,8 @@ $$
 The next figure shows a simulation of 100 elements of this sequence for three
 different discount factors (and hence three different policies).
 
-```{figure} /_static/lecture_specific/optgrowth/solution_og_ex2.png
+```{image} /_static/lecture_specific/optgrowth/solution_og_ex2.png
+:align: center
 ```
 
 In each sequence, the initial condition is $y_0 = 0.1$.

lectures/pandas_panel.md

@@ -502,7 +502,7 @@ in Europe by age and sex from [Eurostat](https://ec.europa.eu/eurostat/data/data
 The dataset can be accessed with the following link:
 
 ```{code-cell} ipython3
-url3 = 'https://raw.githubusercontent.com/QuantEcon/lecture-python/master/source/_static/lecture_specific/pandas_panel/employ.csv'
+url3 = 'https://github.com/QuantEcon/lecture-python.myst/raw/refs/heads/main/lectures/_static/lecture_specific/pandas_panel/employ.csv'
 ```
 
 Reading in the CSV file returns a panel dataset in long format. Use `.pivot_table()` to construct