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🌐 [translation-sync] [python_by_example.md] Update np.random → Generator API #59

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7a3e6a6
Update translation: lectures/python_by_example.md
mmcky Apr 25, 2026
fd6f970
Update translation: .translate/state/python_by_example.md.yml
mmcky Apr 25, 2026
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8 changes: 4 additions & 4 deletions .translate/state/python_by_example.md.yml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
source-sha: 1a87942398e15e03539083cc944a78653c532607
synced-at: "2026-03-20"
source-sha: 3241a07f00b1fb4d2bebc6989d1f480847e08e31
synced-at: "2026-04-25"
model: claude-sonnet-4-6
mode: NEW
mode: UPDATE
section-count: 6
tool-version: 0.13.0
tool-version: 0.14.1
39 changes: 21 additions & 18 deletions lectures/python_by_example.md
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Expand Up @@ -55,7 +55,6 @@ translation:

在开始本讲座之前，您应该已经阅读过关于 Python 入门的{doc}`讲座 <getting_started>`。


## 任务：绘制白噪声过程

假设我们想要模拟并绘制白噪声过程 $\epsilon_0, \epsilon_1, \ldots, \epsilon_T$，其中每个抽取值 $\epsilon_t$ 是独立的标准正态分布。
Expand Down Expand Up @@ -84,7 +83,8 @@ FONTPATH = "_fonts/SourceHanSerifSC-SemiBold.otf" # i18n
mpl.font_manager.fontManager.addfont(FONTPATH) # i18n
mpl.rcParams['font.family'] = ['Source Han Serif SC'] # i18n

ϵ_values = np.random.randn(100)
rng = np.random.default_rng()
ϵ_values = rng.standard_normal(100)
plt.plot(ϵ_values)
plt.show()
```
Expand Down Expand Up @@ -115,7 +115,6 @@ np.sqrt(4)
np.log(4)
```


#### 为什么需要这么多导入语句？

Python 程序通常需要多条导入语句。
Expand All @@ -124,7 +123,6 @@ Python 程序通常需要多条导入语句。

当您想用 Python 做一些有趣的事情时，几乎总是需要导入额外的功能。


#### 包

```{index} single: Python; Packages
Expand Down Expand Up @@ -155,7 +153,7 @@ print(np.__file__)
```{index} single: Python; Subpackages
```

考虑这行代码 `ϵ_values = np.random.randn(100)`。
考虑这行代码 `rng = np.random.default_rng()`。

这里 `np` 指的是 NumPy 包，而 `random` 是 NumPy 的一个**子包**。

Expand Down Expand Up @@ -194,7 +192,7 @@ sqrt(4)
回到我们绘制白噪声的程序，导入语句之后的剩余三行是

```{code-cell} ipython
ϵ_values = np.random.randn(100)
ϵ_values = rng.standard_normal(100)
plt.plot(ϵ_values)
Comment on lines 192 to 196
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Copilot AI Apr 25, 2026

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This section says “导入语句之后的剩余三行是…”, but the updated program now has an extra required line (rng = np.random.default_rng()) before rng.standard_normal(...). As written, the snippet will also fail if executed on its own because rng isn’t defined in this cell. Consider either (a) updating the text to reflect the correct number of lines and including the rng = ... line in this code cell, or (b) explicitly stating this cell assumes rng was created earlier.

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plt.show()
```
Expand Down Expand Up @@ -223,7 +221,7 @@ ts_length = 100
ϵ_values = [] # 空列表

for i in range(ts_length):
e = np.random.randn()
e = rng.standard_normal()
ϵ_values.append(e)

plt.plot(ϵ_values)
Expand Down Expand Up @@ -312,7 +310,7 @@ x[1] # x 的第二个元素

```{code-cell} python3
for i in range(ts_length):
e = np.random.randn()
e = rng.standard_normal()
ϵ_values.append(e)
```

Expand Down Expand Up @@ -345,7 +343,6 @@ Python 解释器执行以下操作：

* 对于 `sequence` 的每个元素，它将名称 `variable_name` "绑定"到该元素，然后执行代码块。


### 关于缩进的说明

```{index} single: Python; Indentation
Expand Down Expand Up @@ -387,7 +384,7 @@ ts_length = 100
ϵ_values = []
i = 0
while i < ts_length:
e = np.random.randn()
e = rng.standard_normal()
ϵ_values.append(e)
i = i + 1
plt.plot(ϵ_values)
Expand Down Expand Up @@ -487,9 +484,10 @@ import matplotlib.pyplot as plt
T = 200
x = np.empty(T+1)
x[0] = 0
rng = np.random.default_rng()

for t in range(T):
x[t+1] = α * x[t] + np.random.randn()
x[t+1] = α * x[t] + rng.standard_normal()

plt.plot(x)
plt.show()
Expand Down Expand Up @@ -527,11 +525,12 @@ plt.show()
α_values = [0.0, 0.8, 0.98]
T = 200
x = np.empty(T+1)
rng = np.random.default_rng()

for α in α_values:
x[0] = 0
for t in range(T):
x[t+1] = α * x[t] + np.random.randn()
x[t+1] = α * x[t] + rng.standard_normal()
plt.plot(x, label=f'$\\alpha = {α}$')

plt.legend()
Expand Down Expand Up @@ -579,9 +578,10 @@ $$
T = 200
x = np.empty(T+1)
x[0] = 0
rng = np.random.default_rng()

for t in range(T):
x[t+1] = α * np.abs(x[t]) + np.random.randn()
x[t+1] = α * np.abs(x[t]) + rng.standard_normal()

plt.plot(x)
plt.show()
Expand Down Expand Up @@ -631,13 +631,14 @@ for x in numbers:
T = 200
x = np.empty(T+1)
x[0] = 0
rng = np.random.default_rng()

for t in range(T):
if x[t] < 0:
abs_x = - x[t]
else:
abs_x = x[t]
x[t+1] = α * abs_x + np.random.randn()
x[t+1] = α * abs_x + rng.standard_normal()

plt.plot(x)
plt.show()
Expand All @@ -650,10 +651,11 @@ plt.show()
T = 200
x = np.empty(T+1)
x[0] = 0
rng = np.random.default_rng()

for t in range(T):
abs_x = - x[t] if x[t] < 0 else x[t]
x[t+1] = α * abs_x + np.random.randn()
x[t+1] = α * abs_x + rng.standard_normal()

plt.plot(x)
plt.show()
Expand All @@ -670,7 +672,7 @@ plt.show()

这是一个需要一些思考和规划的较难练习。

任务是使用[蒙特卡洛](https://en.wikipedia.org/wiki/Monte_Carlo_method)方法计算 $\pi$ 的近似值。
任务是使用 [蒙特卡洛](https://en.wikipedia.org/wiki/Monte_Carlo_method) 方法计算 $\pi$ 的近似值。

除以下内容外不使用其他导入语句

Expand Down Expand Up @@ -710,12 +712,13 @@ import numpy as np

```{code-cell} python3
n = 1000000 # 蒙特卡洛模拟的样本量
rng = np.random.default_rng()

count = 0
for i in range(n):

# 在正方形上随机抽取位置
u, v = np.random.uniform(), np.random.uniform()
u, v = rng.uniform(), rng.uniform()

# 检查该点是否落在以 (0.5,0.5) 为圆心的
# 单位圆的边界内
Expand All @@ -732,4 +735,4 @@ print(area_estimate * 4) # 除以半径的平方
```

```{solution-end}
```
```
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