Fix ValueError in opt_tax_recur.md and amss Lectures (#145)

* ENH: Upgrade to anaconda==2023.07

* fix ValueError across all lectures

* temporarily block build fold download

* restore CI on build folder

---------

Co-authored-by: mmcky <mamckay@gmail.com>

environment.yml

@@ -3,11 +3,12 @@ channels:
   - default
 dependencies:
   - python=3.10
-  - anaconda=2023.03
+  - anaconda=2023.07
   - pip
   - pip:
     - jupyter-book==0.15.1
-    - quantecon-book-theme==0.4.1
+    - docutils==0.17.1
+    - quantecon-book-theme==0.5.3
     - sphinx-tojupyter==0.3.0
     - sphinxext-rediraffe==0.2.7
     - sphinx-exercise==0.4.1

lectures/_static/lecture_specific/amss2/recursive_allocation.py

@@ -142,7 +142,7 @@ def simulate(self, B_, s_0, T, sHist=None):
 
             cHist[t], nHist[t], Bhist[t], ΤHist[t] = c[s], n[s], x / Eu_c, Τ
             xHist[t], THist[t] = xprime[s], T[s]
-        return np.array([cHist, nHist, Bhist, ΤHist, THist, μHist, sHist, xHist])
+        return [cHist, nHist, Bhist, ΤHist, THist, μHist, sHist, xHist]
 
 
 class BellmanEquation:

lectures/_static/lecture_specific/amss2/sequential_allocation.py

@@ -155,4 +155,4 @@ def simulate(self, B_, s_0, T, sHist=None):
             RHist[t - 1] = Uc(cHist[t - 1], nHist[t - 1]) / (β * Eu_c)
             μHist[t] = μ
 
-        return np.array([cHist, nHist, Bhist, ΤHist, sHist, μHist, RHist])
+        return [cHist, nHist, Bhist, ΤHist, sHist, μHist, RHist]

lectures/_static/lecture_specific/opt_tax_recur/sequential_allocation.py

@@ -127,7 +127,7 @@ def implementability(self, Φ, b0, s0, cn0_arr):
         n0 = c0 + g0
         l0 = 1 - n0
 
-        cn0_arr[:] = c0, n0
+        cn0_arr[:] = c0.item(), n0.item()
 
         LHS = Uc(c0, l0) * b0
         RHS = Uc(c0, l0) * c0 - Ul(c0, l0) * n0 + β * π[s0] @ x

lectures/amss2.md

@@ -412,7 +412,7 @@ def solve_cb(unknowns, Φ, b_bar, s=1):
            + Φ * (c0 * u.Ucc(c0, 1) + (c0 + g0) * u.Unn(1, c0 + g0)) \
            - Φ * u.Ucc(c0, 1) * b0
 
-    return np.array([eq1, eq2], dtype='float64')
+    return np.array([eq1, eq2.item()], dtype='float64')
 ```
 
 To solve the equations for $c_0, b_0$, we use SciPy's fsolve function