[likelihood_ratio_processes] Update structure and SyntaxWarnings

.github/workflows/ci.yml

@@ -23,7 +23,7 @@ jobs:
         run: |
           pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128
           pip install pyro-ppl
-          pip install --upgrade "jax[cuda12-local]"
+          pip install --upgrade "jax[cuda12-local]==0.6.2"
           pip install numpyro pyro-ppl
           python scripts/test-jax-install.py
       - name: Check nvidia Drivers

lectures/finite_markov.md

@@ -1077,7 +1077,7 @@ for x0, col in ((0, 'blue'), (1, 'green')):
     X_bar = (X == 0).cumsum() / (1 + np.arange(N, dtype=float))
     # Plot
     ax.fill_between(range(N), np.zeros(N), X_bar - p, color=col, alpha=0.1)
-    ax.plot(X_bar - p, color=col, label=f'$X_0 = \, {x0} $')
+    ax.plot(X_bar - p, color=col, label=fr'$X_0 = \, {x0} $')
     # Overlay in black--make lines clearer
     ax.plot(X_bar - p, 'k-', alpha=0.6)
 
@@ -1280,7 +1280,7 @@ Q = np.zeros((n, n), dtype=int)
 with open(infile) as f:
     edges = f.readlines()
 for edge in edges:
-    from_node, to_node = re.findall('\w', edge)
+    from_node, to_node = re.findall(r'\w', edge)
     i, j = alphabet.index(from_node), alphabet.index(to_node)
     Q[i, j] = 1
 # Create the corresponding Markov matrix P

lectures/imp_sample.md

@@ -90,7 +90,7 @@ w_range = np.linspace(1e-2, 1-1e-5, 1000)
 
 plt.plot(w_range, g(w_range), label='g')
 plt.plot(w_range, f(w_range), label='f')
-plt.xlabel('$\omega$')
+plt.xlabel(r'$\omega$')
 plt.legend()
 plt.title('density functions $f$ and $g$')
 plt.show()
@@ -104,8 +104,8 @@ l = jit(lambda w: f(w) / g(w))
 
 ```{code-cell} ipython3
 plt.plot(w_range, l(w_range))
-plt.title('$\ell(\omega)$')
-plt.xlabel('$\omega$')
+plt.title(r'$\ell(\omega)$')
+plt.xlabel(r'$\omega$')
 plt.show()
 ```
 
@@ -314,7 +314,7 @@ for i, t in enumerate([1, 5, 10, 20]):
     for n, bins, μ_hat, σ_hat in [[n_p, bins_p, μ_hat_p, σ_hat_p],
                                   [n_q, bins_q, μ_hat_q, σ_hat_q]]:
         idx = np.argmax(n)
-        axs[row, col].text(bins[idx], n[idx], '$\hat{μ}$='+f'{μ_hat:.4g}'+', $\hat{σ}=$'+f'{σ_hat:.4g}')
+        axs[row, col].text(bins[idx], n[idx], r'$\hat{μ}$='+f'{μ_hat:.4g}'+r', $\hat{σ}=$'+f'{σ_hat:.4g}')
 
 plt.show()
 ```
@@ -418,7 +418,7 @@ for i, t in enumerate([1, 20]):
     for n, bins, μ_hat, σ_hat in [[n_p, bins_p, μ_hat_p, σ_hat_p],
                                   [n_q, bins_q, μ_hat_q, σ_hat_q]]:
         idx = np.argmax(n)
-        axs[i].text(bins[idx], n[idx], '$\hat{μ}$='+f'{μ_hat:.4g}'+', $\hat{σ}=$'+f'{σ_hat:.4g}')
+        axs[i].text(bins[idx], n[idx], r'$\hat{μ}$='+f'{μ_hat:.4g}'+r', $\hat{σ}=$'+f'{σ_hat:.4g}')
 
 plt.show()
 ```
@@ -452,7 +452,7 @@ for i, t in enumerate([1, 20]):
     for n, bins, μ_hat, σ_hat in [[n_p, bins_p, μ_hat_p, σ_hat_p],
                                   [n_q, bins_q, μ_hat_q, σ_hat_q]]:
         idx = np.argmax(n)
-        axs[i].text(bins[idx], n[idx], '$\hat{μ}$='+f'{μ_hat:.4g}'+', $\hat{σ}=$'+f'{σ_hat:.4g}')
+        axs[i].text(bins[idx], n[idx], r'$\hat{μ}$='+f'{μ_hat:.4g}'+r', $\hat{σ}=$'+f'{σ_hat:.4g}')
 
 plt.show()
 ```