FIX: Deprecation and Future Warnings (#444)

* updates to perm_income_cons

* check JAX deprecation fix

* fix deprecation in jax

* fix linear_models

* fix kalman.md

* remove testing variable

* kalman - remove debug

* fix deprecations warnings in kalman_2

* fix markov_perf deprecations

* review kesten_processes and looking OK

* fix pandas_panel deprecations

* fix string formatting warning

* fix missing solution-end makers

* fix missing solution end

* ensure latex is installed for rendering of plot (collab)

* Revert "ensure latex is installed for rendering of plot (collab)"

This reverts commit b62d770.

* move texlive install for collab to action

* check pickled environment failure

* [linear_models] final deprecation notice

* remove debug

* add gpu backend code to the top of the lecture

Author: mmcky

.github/workflows/collab.yml

@@ -10,6 +10,12 @@ jobs:
       - uses: actions/checkout@v4
         with:
           ref: ${{ github.event.pull_request.head.sha }}
+      # Install build software
+      - name: Install Build Software & LaTeX (kalman_2)
+        shell: bash -l {0}
+        run: |
+          pip install jupyter-book==1.0.3 quantecon-book-theme==0.8.2 sphinx-tojupyter==0.3.0 sphinxext-rediraffe==0.2.7 sphinxcontrib-youtube==1.3.0 sphinx-togglebutton==0.3.2 arviz sphinx-proof sphinx-exercise sphinx-reredirects
+          apt-get install dvipng texlive texlive-latex-extra texlive-fonts-recommended cm-super    
       - name: Check nvidia drivers
         shell: bash -l {0}
         run: |
@@ -28,11 +34,6 @@ jobs:
           branch: main
           name: build-cache
           path: _build
-      # Install build software
-      - name: Install Build Software
-        shell: bash -l {0}
-        run: |
-          pip install jupyter-book==1.0.3 quantecon-book-theme==0.8.2 sphinx-tojupyter==0.3.0 sphinxext-rediraffe==0.2.7 sphinxcontrib-youtube==1.3.0 sphinx-togglebutton==0.3.2 arviz sphinx-proof sphinx-exercise sphinx-reredirects
       # Build of HTML (Execution Testing)
       - name: Build HTML
         shell: bash -l {0}

lectures/back_prop.md

@@ -4,9 +4,9 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.11.5
+    jupytext_version: 1.16.7
 kernelspec:
-  display_name: Python 3
+  display_name: Python 3 (ipykernel)
   language: python
   name: python3
 ---
@@ -22,6 +22,12 @@ kernelspec:
 !pip install --upgrade jax
 ```
 
+```{code-cell} ipython3
+import jax
+## to check that gpu is activated in environment
+print(f"JAX backend: {jax.devices()[0].platform}")
+```
+
 In addition to what's included in base Anaconda, we need to install the following packages
 
 ```{code-cell} ipython3
@@ -604,15 +610,4 @@ Image(fig.to_image(format="png"))
 # notebook locally
 ```
 
-```{code-cell} ipython3
-## to check that gpu is activated in environment
-
-from jax.lib import xla_bridge
-print(xla_bridge.get_backend().platform)
-```
 
-```{note}
-**Cloud Environment:** This lecture site is built in a server environment that doesn't have access to a `gpu`
-If you run this lecture locally this lets you know where your code is being executed, either
-via the `cpu` or the `gpu`
-```

lectures/cass_fiscal.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.6
+    jupytext_version: 1.16.7
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -750,7 +750,7 @@ def plot_results(solution, k_ss, c_ss, shocks, shock_param,
     R_bar_path = compute_R_bar_path(shocks, k_path, model, S)
 
     axes[2].plot(R_bar_path[:T], linestyle=linestyle, label=label)
-    axes[2].set_title('$\overline{R}$')
+    axes[2].set_title(r'$\overline{R}$')
     axes[2].axhline(1 / model.β, linestyle='--', color='black')
     
     η_path = compute_η_path(k_path, model, S=T)
@@ -1041,7 +1041,7 @@ Indeed, {eq}`eq:euler_house` or {eq}`eq:diff_second` indicates that a foreseen i
 crease in $\tau_{ct}$ (i.e., a decrease in $(1+\tau_{ct})$
 $(1+\tau_{ct+1})$) operates like an increase in $\tau_{kt}$.
 
-The following figure portrays the response to a foreseen increase in the consumption tax $\tau_c$. 
+The following figure portrays the response to a foreseen increase in the consumption tax $\tau_c$.
 
 ```{code-cell} ipython3
 shocks = {
@@ -1101,7 +1101,6 @@ The  figure shows that:
 - Transition dynamics push $k_t$ (capital stock) toward a new, lower steady-state level. In the new steady state:
     - Consumption is lower due to reduced output from the lower capital stock.
     - Smoother consumption paths occur when $\gamma = 2$ than when $\gamma = 0.2$.
-    
 
 +++
 
@@ -1111,8 +1110,6 @@ foreseen one-time change in a policy variable (a "pulse").
 
 **Experiment 4: Foreseen one-time increase in $g$ from 0.2 to 0.4 in period 10, after which $g$ returns to 0.2 forever**
 
-
-
 ```{code-cell} ipython3
 g_path = np.repeat(0.2, S + 1)
 g_path[10] = 0.4
@@ -1136,6 +1133,7 @@ The figure indicates how:
     - Before $t = 10$, capital accumulates as interest rate changes induce households to prepare for the anticipated increase in government spending.
     - At $t = 10$, the capital stock sharply decreases as the government consumes part of it.
     - $\bar{R}$ jumps above its steady-state value due to the capital reduction and then gradually declines toward its steady-state level.
+
 +++
 
 ### Method 2: Residual Minimization 

lectures/kalman.md

@@ -3,8 +3,10 @@ jupytext:
   text_representation:
     extension: .md
     format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.16.7
 kernelspec:
-  display_name: Python 3
+  display_name: Python 3 (ipykernel)
   language: python
   name: python3
 ---
@@ -29,10 +31,9 @@ kernelspec:
 
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
-```{code-cell} ipython
----
-tags: [hide-output]
----
+```{code-cell} ipython3
+:tags: [hide-output]
+
 !pip install quantecon
 ```
 
@@ -54,9 +55,8 @@ Required knowledge: Familiarity with matrix manipulations, multivariate normal d
 
 We'll need the following imports:
 
-```{code-cell} ipython
+```{code-cell} ipython3
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 from scipy import linalg
 import numpy as np
 import matplotlib.cm as cm
@@ -122,10 +122,9 @@ $2 \times 2$ covariance matrix.  In our simulations, we will suppose that
 
 This density $p(x)$ is shown below as a contour map, with the center of the red ellipse being equal to $\hat x$.
 
-```{code-cell} python3
----
-tags: [output_scroll]
----
+```{code-cell} ipython3
+:tags: [output_scroll]
+
 # Set up the Gaussian prior density p
 Σ = [[0.4, 0.3], [0.3, 0.45]]
 Σ = np.matrix(Σ)
@@ -186,7 +185,7 @@ def bivariate_normal(x, y, σ_x=1.0, σ_y=1.0, μ_x=0.0, μ_y=0.0, σ_xy=0.0):
 
 def gen_gaussian_plot_vals(μ, C):
     "Z values for plotting the bivariate Gaussian N(μ, C)"
-    m_x, m_y = float(μ[0]), float(μ[1])
+    m_x, m_y = float(μ[0,0]), float(μ[1,0])
     s_x, s_y = np.sqrt(C[0, 0]), np.sqrt(C[1, 1])
     s_xy = C[0, 1]
     return bivariate_normal(X, Y, s_x, s_y, m_x, m_y, s_xy)
@@ -213,15 +212,15 @@ The good news is that the missile has been located by our sensors, which report
 The next figure shows the original prior $p(x)$ and the new reported
 location $y$
 
-```{code-cell} python3
+```{code-cell} ipython3
 fig, ax = plt.subplots(figsize=(10, 8))
 ax.grid()
 
 Z = gen_gaussian_plot_vals(x_hat, Σ)
 ax.contourf(X, Y, Z, 6, alpha=0.6, cmap=cm.jet)
 cs = ax.contour(X, Y, Z, 6, colors="black")
 ax.clabel(cs, inline=1, fontsize=10)
-ax.text(float(y[0]), float(y[1]), "$y$", fontsize=20, color="black")
+ax.text(float(y[0].item()), float(y[1].item()), "$y$", fontsize=20, color="black")
 
 plt.show()
 ```
@@ -284,7 +283,7 @@ This new density $p(x \,|\, y) = N(\hat x^F, \Sigma^F)$ is shown in the next fig
 
 The original density is left in as contour lines for comparison
 
-```{code-cell} python3
+```{code-cell} ipython3
 fig, ax = plt.subplots(figsize=(10, 8))
 ax.grid()
 
@@ -298,7 +297,7 @@ new_Z = gen_gaussian_plot_vals(x_hat_F, Σ_F)
 cs2 = ax.contour(X, Y, new_Z, 6, colors="black")
 ax.clabel(cs2, inline=1, fontsize=10)
 ax.contourf(X, Y, new_Z, 6, alpha=0.6, cmap=cm.jet)
-ax.text(float(y[0]), float(y[1]), "$y$", fontsize=20, color="black")
+ax.text(float(y[0].item()), float(y[1].item()), "$y$", fontsize=20, color="black")
 
 plt.show()
 ```
@@ -391,7 +390,7 @@ A
 Q = 0.3 * \Sigma
 $$
 
-```{code-cell} python3
+```{code-cell} ipython3
 fig, ax = plt.subplots(figsize=(10, 8))
 ax.grid()
 
@@ -415,7 +414,7 @@ new_Z = gen_gaussian_plot_vals(new_x_hat, new_Σ)
 cs3 = ax.contour(X, Y, new_Z, 6, colors="black")
 ax.clabel(cs3, inline=1, fontsize=10)
 ax.contourf(X, Y, new_Z, 6, alpha=0.6, cmap=cm.jet)
-ax.text(float(y[0]), float(y[1]), "$y$", fontsize=20, color="black")
+ax.text(float(y[0].item()), float(y[1].item()), "$y$", fontsize=20, color="black")
 
 plt.show()
 ```
@@ -577,7 +576,7 @@ Your figure should -- modulo randomness -- look something like this
 :class: dropdown
 ```
 
-```{code-cell} python3
+```{code-cell} ipython3
 # Parameters
 θ = 10  # Constant value of state x_t
 A, C, G, H = 1, 0, 1, 1
@@ -598,7 +597,7 @@ xgrid = np.linspace(θ - 5, θ + 2, 200)
 
 for i in range(N):
     # Record the current predicted mean and variance
-    m, v = [float(z) for z in (kalman.x_hat, kalman.Sigma)]
+    m, v = [float(z) for z in (kalman.x_hat.item(), kalman.Sigma.item())]
     # Plot, update filter
     ax.plot(xgrid, norm.pdf(xgrid, loc=m, scale=np.sqrt(v)), label=f'$t={i}$')
     kalman.update(y[i])
@@ -641,7 +640,7 @@ Your figure should show error erratically declining something like this
 :class: dropdown
 ```
 
-```{code-cell} python3
+```{code-cell} ipython3
 ϵ = 0.1
 θ = 10  # Constant value of state x_t
 A, C, G, H = 1, 0, 1, 1
@@ -657,7 +656,7 @@ y = y.flatten()
 
 for t in range(T):
     # Record the current predicted mean and variance and plot their densities
-    m, v = [float(temp) for temp in (kalman.x_hat, kalman.Sigma)]
+    m, v = [float(temp) for temp in (kalman.x_hat.item(), kalman.Sigma.item())]
 
     f = lambda x: norm.pdf(x, loc=m, scale=np.sqrt(v))
     integral, error = quad(f, θ - ϵ, θ + ϵ)
@@ -745,7 +744,7 @@ Observe how, after an initial learning period, the Kalman filter performs quite
 :class: dropdown
 ```
 
-```{code-cell} python3
+```{code-cell} ipython3
 # Define A, C, G, H
 G = np.identity(2)
 H = np.sqrt(0.5) * np.identity(2)