Merge branch 'main' into centralize_figure

QuantEcon · Jun 21, 2021 · 59046d7 · 59046d7 · github-actions · Jun 21, 2021
2 parents f833153 + 7d072c7
commit 59046d7
Show file tree

Hide file tree

Showing 10 changed files with 20 additions and 16 deletions.
diff --git a/environment.yml b/environment.yml
@@ -8,7 +8,7 @@ dependencies:
   - pip:
     - jupyter-book==0.10.2
     - sphinx-multitoc-numbering==0.1.3
-    - quantecon-book-theme==0.2.2
+    - quantecon-book-theme==0.2.4
     - sphinx-tojupyter==0.1.1
     - sphinxext-rediraffe==0.2.7
     - sphinx-exercise==0.1.1

diff --git a/lectures/additive_functionals.md b/lectures/additive_functionals.md
@@ -406,7 +406,7 @@ The code below adds some functions that generate plots for instances of the `AMF
 
 ```{code-cell} python3
 ---
-tags: [output_scroll]
+tags: [collapse-20]
 ---
 def plot_given_paths(amf, T, ypath, mpath, spath, tpath,
                     mbounds, sbounds, horline=0, show_trend=True):

diff --git a/lectures/amss.md b/lectures/amss.md
@@ -403,7 +403,7 @@ on optimal taxation with state-contingent debt  sequential allocation implementa
 ```{code-cell} python3
 ---
 load: _static/lecture_specific/opt_tax_recur/sequential_allocation.py
-tags: [output_scroll]
+tags: [collapse-20]
 ---
 ```
 
@@ -759,6 +759,7 @@ Paths with circles are histories in which there is peace, while those with
 triangle denote war.
 
 ```{code-cell} python3
+:tags: ["scroll-output"]
 # Initialize μgrid for value function iteration
 μ_grid = np.linspace(-0.7, 0.01, 300)
 
@@ -882,6 +883,7 @@ state-contingent debt (circles) and the economy with only a risk-free bond
 (triangles).
 
 ```{code-cell} python3
+:tags: ["scroll-output"]
 log_example = LogUtility()
 log_example.transfers = True                    # Government can use transfers
 log_sequential = SequentialAllocation(log_example)  # Solve sequential problem

diff --git a/lectures/amss2.md b/lectures/amss2.md
@@ -279,21 +279,21 @@ The code is  mostly taken or adapted from the earlier lectures {doc}`optimal tax
 ```{code-cell} python3
 ---
 load: _static/lecture_specific/opt_tax_recur/sequential_allocation.py
-tags: [output_scroll]
+tags: [collapse-20]
 ---
 ```
 
 ```{code-cell} python3
 ---
 load: _static/lecture_specific/amss/recursive_allocation.py
-tags: [output_scroll]
+tags: [collapse-20]
 ---
 ```
 
 ```{code-cell} python3
 ---
 load: _static/lecture_specific/amss/utilities.py
-tags: [output_scroll]
+tags: [collapse-20]
 ---
 ```
 
@@ -433,6 +433,7 @@ debt equal to $b_0 = -1.038698407551764$.
 These graphs report outcomes for both the Lucas-Stokey economy with complete markets and the AMSS economy with one-period risk-free debt only.
 
 ```{code-cell} python3
+:tags: ["scroll-output"]
 μ_grid = np.linspace(-0.09, 0.1, 100)
 
 log_example = CRRAutility()

diff --git a/lectures/amss3.md b/lectures/amss3.md
@@ -152,21 +152,21 @@ Here it is
 ```{code-cell} python3
 ---
 load: _static/lecture_specific/opt_tax_recur/sequential_allocation.py
-tags: [output_scroll]
+tags: [collapse-20]
 ---
 ```
 
 ```{code-cell} python3
 ---
 load: _static/lecture_specific/amss/recursive_allocation.py
-tags: [output_scroll]
+tags: [collapse-20]
 ---
 ```
 
 ```{code-cell} python3
 ---
 load: _static/lecture_specific/amss/utilities.py
-tags: [output_scroll]
+tags: [collapse-20]
 ---
 ```
 
@@ -176,6 +176,7 @@ government debt equal to $-.5$.
 Here is a graph of a long simulation of 102000 periods.
 
 ```{code-cell} python3
+:tags: ["scroll-output"]
 μ_grid = np.linspace(-0.09, 0.1, 100)
 
 log_example = CRRAutility(π=(1 / 3) * np.ones((3, 3)),

diff --git a/lectures/chang_credible.md b/lectures/chang_credible.md
@@ -810,7 +810,7 @@ The following code computes sustainable plans
 ```{code-cell} python3
 ---
 load: _static/lecture_specific/chang_credible/changecon.py
-tags: [output_scroll]
+tags: [collapse-20]
 ---
 ```
 

diff --git a/lectures/matsuyama.md b/lectures/matsuyama.md
@@ -711,7 +711,7 @@ Dark colors indicate synchronization, while light colors indicate failure to syn
 
 (matsrep)=
 ```{figure} /_static/lecture_specific/matsuyama/matsuyama_14.png
-:scale: 60
+:scale: 50
 ```
 
 As you can see, larger values of $\rho$ translate to more synchronization.
@@ -723,7 +723,7 @@ In the solution to the exercises, you'll also find a figure with sliders, allowi
 Here's one snapshot from the interactive figure
 
 ```{figure} /_static/lecture_specific/matsuyama/matsuyama_18.png
-
+:scale: 80
 ```
 
 ## Exercises

diff --git a/lectures/orth_proj.md b/lectures/orth_proj.md
@@ -329,7 +329,7 @@ $$
 
 Evidently  $Py$ is a linear function from $y \in \mathbb R^n$ to $P y \in \mathbb R^n$.
 
-This reference is useful [https://en.wikipedia.org/wiki/Linear_map#Matrices](https://en.wikipedia.org/wiki/Linear_map#Matrices).
+[This reference](https://en.wikipedia.org/wiki/Linear_map#Matrices) is useful.
 
 **Theorem.** Let the columns of $n \times k$ matrix $X$ form a basis of $S$.  Then
 
@@ -340,7 +340,7 @@ $$
 Proof: Given arbitrary $y \in \mathbb R^n$ and $P = X (X'X)^{-1} X'$, our claim is that
 
 1. $P y \in S$, and
-1. $y - P y \perp S$
+2. $y - P y \perp S$
 
 Claim 1 is true because
 

diff --git a/lectures/smoothing.md b/lectures/smoothing.md
@@ -115,7 +115,7 @@ payoffs depend on next period's realization of the Markov state.
 * In an $N$ state Markov state version,  $N$ such securities are traded each period.
 * In a continuous state Markov state version, a continuum of such securities are traded each period.
 
-These state-contingent securities are commonly called Arrow securities, after Kenneth Arrow <https://en.wikipedia.org/wiki/Kenneth_Arrow>
+These state-contingent securities are commonly called Arrow securities, after [Kenneth Arrow](https://en.wikipedia.org/wiki/Kenneth_Arrow).
 
 In the **incomplete markets version**, the consumer can buy and sell only one security each period, a risk-free one-period bond with gross
 one-period return $\beta^{-1}$.

diff --git a/lectures/von_neumann_model.md b/lectures/von_neumann_model.md
@@ -61,7 +61,7 @@ The code below provides the `Neumann` class
 
 ```{code-cell} python3
 ---
-tags: [output_scroll]
+tags: [collapse-20]
 ---
 class Neumann(object):