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🌐 [translation-sync] [python_by_example.md] Update np.random → Generator API #114

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Update translation: lectures/python_by_example.md
mmcky Apr 25, 2026
aa4fb1b
Update translation: .translate/state/python_by_example.md.yml
mmcky Apr 25, 2026
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10 changes: 5 additions & 5 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"
model: unknown
mode: RESYNC
source-sha: 3241a07f00b1fb4d2bebc6989d1f480847e08e31
synced-at: "2026-04-25"
model: claude-sonnet-4-6
mode: UPDATE
section-count: 6
tool-version: 0.13.0
tool-version: 0.14.1
37 changes: 20 additions & 17 deletions lectures/python_by_example.md
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Expand Up @@ -53,7 +53,6 @@ translation:

پیش از شروع این درس، باید جلسه ی قبل را مطالعه کرده باشید.


## هدف:رسم یک فرآیند نویز سفید

فرض کنید می‌خواهیم فرآیند نویز سفید $\epsilon_0, \epsilon_1, \ldots, \epsilon_T$ را شبیه‌سازی و رسم کنیم، که در آن هر نقطه $\epsilon_t$ مستقل و نرمال استاندارد است.
Expand All @@ -77,7 +76,8 @@ translation:
import numpy as np
import matplotlib.pyplot as plt

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 @@ -111,7 +111,6 @@ np.sqrt(4)
np.log(4)
```


#### چرا در برنامه نویسی پایتون، از Importهای متعددی استفاده می شود؟

برنامه های پایتون معمولا به چندین دستور `import` نیاز دارند.
Expand All @@ -120,7 +119,6 @@ np.log(4)

وقتی بخواهید کارهای جالب تر و پیشرفته تری با پایتون انجام دهید، تقریبا همیشه باید قابلیت های اضافی را با استفاده از `import` به برنامه اضافه کنید.


#### پکیج ها(Packages)

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

به خط حاوی `ϵ_values = np.random.randn(100)` در کد توجه کنید.
به خط حاوی `rng = np.random.default_rng()` در کد توجه کنید.

در این دستور، `np` به پکیج NumPY اشاره دارد، و `random` یک زیرپکیج از NumPY است.

Expand Down Expand Up @@ -191,7 +189,7 @@ sqrt(4)
بیاید به کدی که نویز سفید رسم می کند برگردیم. سه خط بعد از دستور Import به این صورت هستند:

```{code-cell} ipython
ϵ_values = np.random.randn(100)
ϵ_values = rng.standard_normal(100)
plt.plot(ϵ_values)
Comment on lines 189 to 193
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Copilot AI Apr 25, 2026

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The narrative says “three lines after the import” and the snippet starts with ϵ_values = rng.standard_normal(100), but the updated program now includes rng = np.random.default_rng() immediately after the imports. This section should be updated so the description and the displayed snippet reflect the new extra line (e.g., adjust the wording/count and/or include the rng = ... line in the snippet).

Copilot uses AI. Check for mistakes.
plt.show()
```
Expand All @@ -217,7 +215,7 @@ ts_length = 100
ϵ_values = [] # empty list

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 @@ -313,7 +311,7 @@ x[1] # second element of x

```{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 @@ -346,7 +344,6 @@ for variable_name in sequence:

* برای هر عنصر از دنباله `sequence`، نام متغیر `variable_name` را به آن عنصر متصل (blind) می کند و سپس بلوک کد را اجرا می کند.


### یادداشتی درباره تورفتگی (Indentation)

```{index} single: Python; Indentation
Expand Down Expand Up @@ -393,7 +390,7 @@ 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 @@ -495,9 +492,10 @@ 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 @@ -536,11 +534,12 @@ 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 @@ -588,9 +587,10 @@ $$
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 @@ -639,13 +639,14 @@ 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 @@ -658,10 +659,11 @@ 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 Down Expand Up @@ -721,12 +723,13 @@ import numpy as np

```{code-cell} python3
n = 1000000 # sample size for Monte Carlo simulation
rng = np.random.default_rng()

count = 0
for i in range(n):

# drawing random positions on the square
u, v = np.random.uniform(), np.random.uniform()
u, v = rng.uniform(), rng.uniform()

# check whether the point falls within the boundary
# of the unit circle centred at (0.5,0.5)
Expand All @@ -743,4 +746,4 @@ print(area_estimate * 4) # dividing by radius**2
```

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