From 7608ea4eb5935f4eb781ffab1666779e7a7f2de1 Mon Sep 17 00:00:00 2001
From: Matt McKay <mmcky@users.noreply.github.com>
Date: Sat, 1 Aug 2026 09:19:04 +1000
Subject: [PATCH 1/2] Update translation: lectures/uncertainty_traps.md

---
 lectures/uncertainty_traps.md | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/lectures/uncertainty_traps.md b/lectures/uncertainty_traps.md
index 3d072095..f643d984 100644
--- a/lectures/uncertainty_traps.md
+++ b/lectures/uncertainty_traps.md
@@ -291,15 +291,15 @@ class UncertaintyTrapEcon:
         """
         self.θ = self.ρ * self.θ + self.σ_θ * w
 
-    def gen_aggregates(self):
+    def gen_aggregates(self, rng):
         """
         基于当前信念(μ, γ)生成总量。这是一个
         依赖于F抽样的模拟步骤。
         """
-        F_vals = self.σ_F * np.random.randn(self.num_firms)
+        F_vals = self.σ_F * rng.standard_normal(self.num_firms)
         M = np.sum(self.ψ(F_vals) > 0)  # 计算活跃企业数量
         if M > 0:
-            x_vals = self.θ + self.σ_x * np.random.randn(M)
+            x_vals = self.θ + self.σ_x * rng.standard_normal(M)
             X = x_vals.mean()
         else:
             X = 0
@@ -404,7 +404,7 @@ $$
 
 这里的 $M$ 是活跃企业的数量。下图在45度图上绘制了不同 $M$ 值下 $\gamma_{t+1}$ 对 $\gamma_t$ 的关系
 
-```{code-cell} ipython3
+```{code-cell} python3
 econ = UncertaintyTrapEcon()
 ρ, σ_θ, γ_x = econ.ρ, econ.σ_θ, econ.γ_x    # 简化名称
 γ = np.linspace(1e-10, 3, 200)              # γ 网格
@@ -426,7 +426,7 @@ plt.show()
 
 接下来让我们生成信念和总量的时间序列数据——即活跃企业数量和平均产出
 
-```{code-cell} ipython3
+```{code-cell} python3
 sim_length=2000
 
 μ_vec = np.empty(sim_length)
@@ -439,10 +439,11 @@ M_vec = np.empty(sim_length)
 γ_vec[0] = econ.γ
 θ_vec[0] = 0
 
-w_shocks = np.random.randn(sim_length)
+rng = np.random.default_rng()
+w_shocks = rng.standard_normal(sim_length)
 
 for t in range(sim_length-1):
-    X, M = econ.gen_aggregates()
+    X, M = econ.gen_aggregates(rng)
     X_vec[t] = X
     M_vec[t] = M
 
@@ -454,14 +455,14 @@ for t in range(sim_length-1):
     θ_vec[t+1] = econ.θ
 
 # 记录总量的最终值
-X, M = econ.gen_aggregates()
+X, M = econ.gen_aggregates(rng)
 X_vec[-1] = X
 M_vec[-1] = M
 ```
 
 首先，让我们看看在这些模拟中 $\mu$ 是如何跟踪 $\theta$ 的
 
-```{code-cell} ipython3
+```{code-cell} python3
 fig, ax = plt.subplots(figsize=(9, 6))
 ax.plot(range(sim_length), θ_vec, alpha=0.6, lw=2, label=r"$\theta$")
 ax.plot(range(sim_length), μ_vec, alpha=0.6, lw=2, label=r"$\mu$")
@@ -472,7 +473,7 @@ plt.show()
 
 现在让我们把所有内容一起绘制出来
 
-```{code-cell} ipython3
+```{code-cell} python3
 fig, axes = plt.subplots(4, 1, figsize=(12, 20))
 # 添加一些间距
 fig.subplots_adjust(hspace=0.3)
@@ -504,4 +505,3 @@ plt.show()
 
 ```{solution-end}
 ```
-

From e70059ba3ca307fab924ea69856000d5085a0b1d Mon Sep 17 00:00:00 2001
From: Matt McKay <mmcky@users.noreply.github.com>
Date: Sat, 1 Aug 2026 09:19:04 +1000
Subject: [PATCH 2/2] Update translation:
 .translate/state/uncertainty_traps.md.yml

---
 .translate/state/uncertainty_traps.md.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.translate/state/uncertainty_traps.md.yml b/.translate/state/uncertainty_traps.md.yml
index dc8139ee..470f14bc 100644
--- a/.translate/state/uncertainty_traps.md.yml
+++ b/.translate/state/uncertainty_traps.md.yml
@@ -1,6 +1,6 @@
-source-sha: a1cd90c5ef6d6f05277318f47fe21eda60fa5e6b
-synced-at: "2025-06-17"
-model: unknown
-mode: RESYNC
+source-sha: b90d96d6aa7cda663577180e8e366aee5bb04a7e
+synced-at: "2026-07-31"
+model: claude-sonnet-5
+mode: UPDATE
 section-count: 5
-tool-version: 0.20.0
+tool-version: 0.24.0