Merge branch 'main' into svd

Author: HumphreyYang

.github/workflows/cache.yml

@@ -7,7 +7,7 @@ jobs:
   cache:
     runs-on: quantecon-gpu-runner
     container:
-      image: ghcr.io/quantecon/lecture-python-container:cuda-12.6.0-anaconda-2024-06-py312
+      image: ghcr.io/quantecon/lecture-python-container:cuda-12.6.0-anaconda-2024-10-py312
       options: --gpus all
     steps:
       - uses: actions/checkout@v4
@@ -31,4 +31,5 @@ jobs:
         uses: actions/upload-artifact@v4
         with:
           name: build-cache
-          path: _build
+          path: _build
+          include-hidden-files: true

.github/workflows/ci.yml

@@ -4,7 +4,7 @@ jobs:
   preview:
     runs-on: quantecon-gpu-runner
     container:
-      image: ghcr.io/quantecon/lecture-python-container:cuda-12.6.0-anaconda-2024-06-py312
+      image: ghcr.io/quantecon/lecture-python-container:cuda-12.6.0-anaconda-2024-10-py312
       options: --gpus all
     steps:
       - uses: actions/checkout@v4

.github/workflows/execution.yml

@@ -8,7 +8,7 @@ jobs:
     if: github.event_name == 'push' && startsWith(github.event.ref, 'refs/tags')
     runs-on: quantecon-gpu-runner
     container:
-      image: ghcr.io/quantecon/lecture-python-container:cuda-12.6.0-anaconda-2024-06-py312
+      image: ghcr.io/quantecon/lecture-python-container:cuda-12.6.0-anaconda-2024-10-py312
       options: --gpus all
     steps:
       - name: Checkout

.github/workflows/publish.yml

@@ -3,12 +3,12 @@ channels:
   - default
 dependencies:
   - python=3.12
-  - anaconda=2024.06
+  - anaconda=2024.10
   - pip
   - pip:
     - jupyter-book==0.15.1
     - docutils==0.17.1
-    - quantecon-book-theme==0.7.1
+    - quantecon-book-theme==0.7.2
     - sphinx-reredirects==0.1.3
     - sphinx-tojupyter==0.3.0
     - sphinxext-rediraffe==0.2.7

environment.yml

@@ -283,7 +283,7 @@ class Household:
 
 # Do the hard work using JIT-ed functions
 
-@jit(nopython=True)
+@jit
 def populate_R(R, a_size, z_size, a_vals, z_vals, r, w):
     n = a_size * z_size
     for s_i in range(n):
@@ -297,7 +297,7 @@ def populate_R(R, a_size, z_size, a_vals, z_vals, r, w):
             if c > 0:
                 R[s_i, new_a_i] = np.log(c)  # Utility
 
-@jit(nopython=True)
+@jit
 def populate_Q(Q, a_size, z_size, Π):
     n = a_size * z_size
     for s_i in range(n):
@@ -307,7 +307,7 @@ def populate_Q(Q, a_size, z_size, Π):
                 Q[s_i, a_i, a_i*z_size + next_z_i] = Π[z_i, next_z_i]
 
 
-@jit(nopython=True)
+@jit
 def asset_marginal(s_probs, a_size, z_size):
     a_probs = np.zeros(a_size)
     for a_i in range(a_size):

lectures/aiyagari.md

@@ -408,7 +408,7 @@ $$
 ```{code-cell} ipython3
 import numpy as np
 import matplotlib.pyplot as plt
-from numba import njit
+from numba import jit
 from quantecon.optimize import brent_max
 ```
 
@@ -433,22 +433,22 @@ Knowing $\hat K$, we can calculate other equilibrium objects.
 Let's first define  some Python helper functions.
 
 ```{code-cell} ipython3
-@njit
+@jit
 def K_to_Y(K, α):
 
     return K ** α
 
-@njit
+@jit
 def K_to_r(K, α):
 
     return α * K ** (α - 1)
 
-@njit
+@jit
 def K_to_W(K, α):
 
     return (1 - α) * K ** α
 
-@njit
+@jit
 def K_to_C(K, D, τ, r, α, β):
 
     # optimal consumption for the old when δ=0
@@ -913,7 +913,7 @@ Let's implement this "guess and verify" approach
 We start by defining the Cobb-Douglas utility function
 
 ```{code-cell} ipython3
-@njit
+@jit
 def U(Cy, Co, β):
 
     return (Cy ** β) * (Co ** (1-β))
@@ -924,7 +924,7 @@ We use `Cy_val` to compute the lifetime value of an arbitrary consumption plan,
 Note that it requires knowing future prices $r_{t+1}$ and tax rate $\tau_{t+1}$.
 
 ```{code-cell} ipython3
-@njit
+@jit
 def Cy_val(Cy, W, r_next, τ, τ_next, δy, δo_next, β):
 
     # Co given by the budget constraint

lectures/ak2.md

@@ -51,7 +51,7 @@ import matplotlib.pyplot as plt
 plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
-from numba import njit, prange
+from numba import jit, prange
 from quantecon.distributions import BetaBinomial
 from scipy.special import binom, beta
 from mpl_toolkits.mplot3d.axes3d import Axes3D
@@ -234,7 +234,7 @@ def operator_factory(cw, parallel_flag=True):
     F_probs, G_probs = cw.F_probs, cw.G_probs
     F_mean, G_mean = cw.F_mean, cw.G_mean
 
-    @njit(parallel=parallel_flag)
+    @jit(parallel=parallel_flag)
     def T(v):
         "The Bellman operator"
 
@@ -249,7 +249,7 @@ def operator_factory(cw, parallel_flag=True):
 
         return v_new
 
-    @njit
+    @jit
     def get_greedy(v):
         "Computes the v-greedy policy"
 
@@ -472,7 +472,7 @@ T, get_greedy = operator_factory(cw)
 v_star = solve_model(cw, verbose=False)
 greedy_star = get_greedy(v_star)
 
-@njit
+@jit
 def passage_time(optimal_policy, F, G):
     t = 0
     i = j = 0
@@ -485,7 +485,7 @@ def passage_time(optimal_policy, F, G):
             i, j  = qe.random.draw(F), qe.random.draw(G)
         t += 1
 
-@njit(parallel=True)
+@jit(parallel=True)
 def median_time(optimal_policy, F, G, M=25000):
     samples = np.empty(M)
     for i in prange(M):

lectures/career.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.1
+    jupytext_version: 1.16.4
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -66,7 +66,7 @@ Let's start with some standard imports:
 
 ```{code-cell} ipython3
 import matplotlib.pyplot as plt
-from numba import njit, float64
+from numba import jit, float64
 from numba.experimental import jitclass
 import numpy as np
 from quantecon.optimize import brentq
@@ -525,7 +525,7 @@ planning problem.
 $c_0$ instead of $\mu_0$ in the following code.)
 
 ```{code-cell} ipython3
-@njit
+@jit
 def shooting(pp, c0, k0, T=10):
     '''
     Given the initial condition of capital k0 and an initial guess
@@ -610,7 +610,7 @@ When $K_{T+1}$ gets close enough to $0$ (i.e., within an error
 tolerance bounds), we stop.
 
 ```{code-cell} ipython3
-@njit
+@jit
 def bisection(pp, c0, k0, T=10, tol=1e-4, max_iter=500, k_ter=0, verbose=True):
 
     # initial boundaries for guess c0
@@ -804,7 +804,7 @@ over time.
 Let's calculate and  plot the saving rate.
 
 ```{code-cell} ipython3
-@njit
+@jit
 def saving_rate(pp, c_path, k_path):
     'Given paths of c and k, computes the path of saving rate.'
     production = pp.f(k_path[:-1])
@@ -912,7 +912,7 @@ $$ (eq:tildeC)
 A positive fixed point  $C = \tilde C(K)$ exists only if $f\left(K\right)+\left(1-\delta\right)K-f^{\prime-1}\left(\frac{1}{\beta}-\left(1-\delta\right)\right)>0$
 
 ```{code-cell} ipython3
-@njit
+@jit
 def C_tilde(K, pp):
 
     return pp.f(K) + (1 - pp.δ) * K - pp.f_prime_inv(1 / pp.β - 1 + pp.δ)
@@ -931,11 +931,11 @@ K = \tilde K(C)
 $$ (eq:tildeK)
 
 ```{code-cell} ipython3
-@njit
+@jit
 def K_diff(K, C, pp):
     return pp.f(K) - pp.δ * K - C
 
-@njit
+@jit
 def K_tilde(C, pp):
 
     res = brentq(K_diff, 1e-6, 100, args=(C, pp))
@@ -951,7 +951,7 @@ It is thus the intersection of the two curves $\tilde{C}$ and $\tilde{K}$ that w
 We can compute $K_s$ by solving the equation $K_s = \tilde{K}\left(\tilde{C}\left(K_s\right)\right)$
 
 ```{code-cell} ipython3
-@njit
+@jit
 def K_tilde_diff(K, pp):
 
     K_out = K_tilde(C_tilde(K, pp), pp)
@@ -1003,7 +1003,7 @@ In addition to the three curves, Figure {numref}`stable_manifold` plots  arrows
 ---
 mystnb:
   figure:
-    caption: "Stable Manifold and Phase Plane"
+    caption: Stable Manifold and Phase Plane
     name: stable_manifold
 tags: [hide-input]
 ---