Merge remote-tracking branch 'origin/main' into jb2

# Conflicts:
#	.github/workflows/ci.yml

Author: mmcky

.github/workflows/cache.yml

@@ -10,7 +10,7 @@ jobs:
     steps:
       - uses: actions/checkout@v6
       - name: Setup Anaconda
-        uses: conda-incubator/setup-miniconda@v3
+        uses: conda-incubator/setup-miniconda@v4
         with:
           auto-update-conda: true
           auto-activate-base: true

.github/workflows/execution-linux.yml

@@ -15,7 +15,7 @@ jobs:
     steps:
       - name: Checkout
         uses: actions/checkout@v6
-      - uses: conda-incubator/setup-miniconda@v3
+      - uses: conda-incubator/setup-miniconda@v4
         with:
           auto-update-conda: true
           python-version: ${{ matrix.python-version }}

.github/workflows/execution-osx.yml

@@ -15,7 +15,7 @@ jobs:
     steps:
       - name: Checkout
         uses: actions/checkout@v6
-      - uses: conda-incubator/setup-miniconda@v3
+      - uses: conda-incubator/setup-miniconda@v4
         with:
           auto-update-conda: true
           python-version: ${{ matrix.python-version }}

.github/workflows/execution-win.yml

@@ -15,7 +15,7 @@ jobs:
     steps:
       - name: Checkout
         uses: actions/checkout@v6
-      - uses: conda-incubator/setup-miniconda@v3
+      - uses: conda-incubator/setup-miniconda@v4
         with:
           auto-update-conda: true
           python-version: ${{ matrix.python-version }}

.github/workflows/publish.yml

@@ -27,7 +27,7 @@ jobs:
         with:
           fetch-depth: 0  # Fetch full git history for changelog feature
       - name: Setup Anaconda
-        uses: conda-incubator/setup-miniconda@v3
+        uses: conda-incubator/setup-miniconda@v4
         with:
           auto-update-conda: true
           auto-activate-base: true
@@ -66,7 +66,7 @@ jobs:
         run: pip list
       # Download Build Cache from cache.yml
       - name: Download "build" folder (cache)
-        uses: dawidd6/action-download-artifact@v20
+        uses: dawidd6/action-download-artifact@v21
         with:
           workflow: cache.yml
           branch: main
@@ -119,7 +119,7 @@ jobs:
           }
           EOF
       - name: Upload archives to release
-        uses: softprops/action-gh-release@v2
+        uses: softprops/action-gh-release@v3
         with:
           files: |
             lecture-python-programming-html-${{ github.ref_name }}.tar.gz
@@ -132,7 +132,7 @@ jobs:
       - name: Setup Pages
         uses: actions/configure-pages@v6
       - name: Upload Pages artifact
-        uses: actions/upload-pages-artifact@v4
+        uses: actions/upload-pages-artifact@v5
         with:
           path: _build/html/
       - name: Deploy to GitHub Pages

.github/workflows/sync-translations-fa.yml

@@ -24,7 +24,7 @@ jobs:
         with:
           fetch-depth: 2
 
-      - uses: QuantEcon/action-translation@v0.14.1
+      - uses: QuantEcon/action-translation@v0.15.0
         with:
           mode: sync
           target-repo: QuantEcon/lecture-python-programming.fa

.github/workflows/sync-translations-zh-cn.yml

@@ -24,7 +24,7 @@ jobs:
         with:
           fetch-depth: 2
 
-      - uses: QuantEcon/action-translation@v0.14.1
+      - uses: QuantEcon/action-translation@v0.15.0
         with:
           mode: sync
           target-repo: QuantEcon/lecture-python-programming.zh-cn

environment.yml

@@ -7,7 +7,7 @@ dependencies:
   - pip
   - pip:
     - jupyter-book>=1.0.4post1,<2.0
-    - quantecon-book-theme==0.20.0
+    - quantecon-book-theme==0.20.3
     - sphinx-tojupyter==0.6.0
     - sphinxext-rediraffe==0.3.0
     - sphinx-exercise==1.2.1

lectures/about_py.md

@@ -451,10 +451,10 @@ Here's some example code that generates and plots a random graph, with node colo
 ```{code-cell} ipython
 import networkx as nx
 import matplotlib.pyplot as plt
-np.random.seed(1234)
+rng = np.random.default_rng(1234)
 
 # Generate a random graph
-p = dict((i, (np.random.uniform(0, 1), np.random.uniform(0, 1)))
+p = dict((i, (rng.uniform(0, 1), rng.uniform(0, 1)))
          for i in range(200))
 g = nx.random_geometric_graph(200, 0.12, pos=p)
 pos = nx.get_node_attributes(g, 'pos')

lectures/numpy.md

@@ -839,7 +839,8 @@ a
 Mutability leads to the following behavior (which can be shocking to MATLAB programmers...)
 
 ```{code-cell} python3
-a = np.random.randn(3)
+rng = np.random.default_rng()
+a = rng.standard_normal(3)
 a
 ```
 
@@ -869,7 +870,7 @@ It is of course possible to make `b` an independent copy of `a` when required.
 This can be done using `np.copy`
 
 ```{code-cell} python3
-a = np.random.randn(3)
+a = rng.standard_normal(3)
 a
 ```
 
@@ -945,7 +946,7 @@ def f(x):
 The NumPy function `np.where` provides a vectorized alternative:
 
 ```{code-cell} python3
-x = np.random.randn(4)
+x = rng.standard_normal(4)
 x
 ```
 
@@ -1020,11 +1021,12 @@ z[z > 3]
 NumPy provides some additional functionality related to scientific programming
 through its sub-packages.
 
-We've already seen how we can generate random variables using np.random
+We've already seen how we can generate random variables using NumPy's 
+[random `Generator`](https://numpy.org/doc/stable/reference/random/generator.html#random-generator).
 
 ```{code-cell} python3
-z = np.random.randn(10000)  # Generate standard normals
-y = np.random.binomial(10, 0.5, size=1000)    # 1,000 draws from Bin(10, 0.5)
+z = rng.standard_normal(10000)  # Generate standard normals
+y = rng.binomial(10, 0.5, size=1000)    # 1,000 draws from Bin(10, 0.5)
 y.mean()
 ```
 
@@ -1064,7 +1066,7 @@ It takes a few seconds to run.
 n = 20
 m = 1000
 for i in range(n):
-    X = np.random.randn(m, m)
+    X = rng.standard_normal((m, m))
     λ = np.linalg.eigvals(X)
 ```
 
@@ -1203,28 +1205,28 @@ Here's our first pass at a solution:
 
 ```{code-cell} python3
 from numpy import cumsum
-from numpy.random import uniform
 
 class DiscreteRV:
     """
     Generates an array of draws from a discrete random variable with vector of
     probabilities given by q.
     """
 
-    def __init__(self, q):
+    def __init__(self, q, seed=None):
         """
         The argument q is a NumPy array, or array like, nonnegative and sums
         to 1
         """
         self.q = q
         self.Q = cumsum(q)
+        self.rng = np.random.default_rng(seed)
 
     def draw(self, k=1):
         """
         Returns k draws from q. For each such draw, the value i is returned
         with probability q[i].
         """
-        return self.Q.searchsorted(uniform(0, 1, size=k))
+        return self.Q.searchsorted(self.rng.uniform(0, 1, size=k))
 ```
 
 The logic is not obvious, but if you take your time and read it slowly,
@@ -1353,7 +1355,8 @@ Here's an example of usage
 
 ```{code-cell} python3
 fig, ax = plt.subplots()
-X = np.random.randn(1000)
+rng = np.random.default_rng()
+X = rng.standard_normal(1000)
 F = ECDF(X)
 F.plot(ax)
 ```
@@ -1374,9 +1377,9 @@ In this exercise, try to use `for` loops to replicate the result of the followin
 
 ```{code-cell} python3
 
-np.random.seed(123)
-x = np.random.randn(4, 4)
-y = np.random.randn(4)
+rng = np.random.default_rng(123)
+x = rng.standard_normal((4, 4))
+y = rng.standard_normal(4)
 A = x / y
 ```
 
@@ -1404,9 +1407,9 @@ Now we can import the quantecon package.
 
 ```{code-cell} python3
 
-np.random.seed(123)
-x = np.random.randn(1000, 100, 100)
-y = np.random.randn(100)
+rng = np.random.default_rng(123)
+x = rng.standard_normal((1000, 100, 100))
+y = rng.standard_normal(100)
 
 with qe.Timer("Broadcasting operation"):
     B = x / y
@@ -1431,9 +1434,9 @@ print(B)
 **Part 1 Solution**
 
 ```{code-cell} python3
-np.random.seed(123)
-x = np.random.randn(4, 4)
-y = np.random.randn(4)
+rng = np.random.default_rng(123)
+x = rng.standard_normal((4, 4))
+y = rng.standard_normal(4)
 
 C = np.empty_like(x)
 n = len(x)
@@ -1461,9 +1464,9 @@ print(np.array_equal(A, C))
 
 ```{code-cell} python3
 
-np.random.seed(123)
-x = np.random.randn(1000, 100, 100)
-y = np.random.randn(100)
+rng = np.random.default_rng(123)
+x = rng.standard_normal((1000, 100, 100))
+y = rng.standard_normal(100)
 
 with qe.Timer("For loop operation"):
     D = np.empty_like(x)