update sources from lecture-python (4ca0fd7) using tomyst (b06cacb)

Author: mmcky

lectures/cass_koopmans_2.md

@@ -835,9 +835,9 @@ for T in T_arr:
     for i, ax in enumerate(axs.flatten()):
         ax.plot(paths[i])
         ax.set(title=titles[i], ylabel=ylabels[i], xlabel='t')
-        if titles[i] is 'Capital':
+        if titles[i] == 'Capital':
             ax.axhline(k_ss, lw=1, ls='--', c='k')
-        if titles[i] is 'Consumption':
+        if titles[i] == 'Consumption':
             ax.axhline(c_ss, lw=1, ls='--', c='k')
 
 plt.tight_layout()
@@ -871,9 +871,9 @@ for γ in γ_arr:
     for i, ax in enumerate(axs.flatten()):
         ax.plot(paths[i], label=f'$\gamma = {γ}$')
         ax.set(title=titles[i], ylabel=ylabels[i], xlabel='t')
-        if titles[i] is 'Capital':
+        if titles[i] == 'Capital':
             ax.axhline(k_ss, lw=1, ls='--', c='k')
-        if titles[i] is 'Consumption':
+        if titles[i] == 'Consumption':
             ax.axhline(c_ss, lw=1, ls='--', c='k')
 
 axs[0, 0].legend()

lectures/complex_and_trig.md

@@ -310,7 +310,7 @@ eq1 = Eq(x1/x0 - r * cos(ω+θ) / cos(ω), 0)
 print(f'ω = {ω:1.3f}')
 
 # Solve for p
-eq2 = Eq(x0 - 2 * p * cos(ω))
+eq2 = Eq(x0 - 2 * p * cos(ω), 0)
 p = nsolve(eq2, p, 0)
 p = np.float(p)
 print(f'p = {p:1.3f}')

lectures/finite_markov.md

@@ -376,7 +376,6 @@ all  ways this can happen and sum their probabilities.
 
 Rewriting this statement in terms of  marginal and conditional probabilities gives
 
-(mc_fdd)=
 $$
 \psi_{t+1}(y) = \sum_{x \in S} P(x,y) \psi_t(x)
 $$
@@ -385,7 +384,6 @@ There are $n$ such equations, one for each $y \in S$.
 
 If we think of $\psi_{t+1}$ and $\psi_t$ as *row vectors* (as is traditional in this literature), these $n$ equations are summarized by the matrix expression
 
-(mc_fddv)=
 ```{math}
 :label: fin_mc_fr
 
@@ -398,7 +396,6 @@ By repeating this $m$ times we move forward $m$ steps into the future.
 
 Hence, iterating on {eq}`fin_mc_fr`, the expression $\psi_{t+m} = \psi_t P^m$ is also valid --- here $P^m$ is the $m$-th power of $P$.
 
-(mc_exfmar)=
 As a special case, we see that if $\psi_0$ is the initial distribution from
 which $X_0$ is drawn, then $\psi_0 P^m$ is the distribution of
 $X_m$.

lectures/mccall_correlated.md

@@ -329,7 +329,7 @@ for c in c_vals:
     js = JobSearch(c=c)
     f_star = compute_fixed_point(js, verbose=False)
     res_wage_function = np.exp(f_star * (1 - js.β))
-    ax.plot(js.z_grid, res_wage_function, label=f"$\\bar w$ at $c = {c}$")
+    ax.plot(js.z_grid, res_wage_function, label=rf"$\bar w$ at $c = {c}$")
 
 ax.set(xlabel="$z$", ylabel="wage")
 ax.legend()
@@ -440,7 +440,7 @@ for i, β in enumerate(beta_vals):
 ```{code-cell} ipython3
 fig, ax = plt.subplots()
 ax.plot(beta_vals, durations)
-ax.set_xlabel("$\\beta$")
+ax.set_xlabel(r"$\beta$")
 ax.set_ylabel("mean unemployment duration")
 plt.show()
 ```

lectures/navy_captain.md

@@ -70,8 +70,8 @@ this lecture {doc}`Exchangeability and Bayesian Updating <exchangeable>` and in
 {doc}`Likelihood Ratio Processes <likelihood_ratio_process>`, which describes the link between Bayesian
 updating and likelihood ratio processes.
 
-The present lecture  uses Python to generate simulations that   evaluate expected losses under  **frequentist** and **Bayesian**
-decision rules for a instances of the Navy Captain's decision problem.
+The present lecture uses Python to generate simulations that evaluate expected losses under **frequentist** and **Bayesian**
+decision rules for an instance of the Navy Captain's decision problem.
 
 The simulations validate the Navy Captain's hunch that there is a better rule than the one the Navy had ordered him
 to use.
@@ -130,7 +130,7 @@ def p(x, a, b):
 ```
 
 We start with defining a `jitclass` that stores parameters and
-functions we need to solve problems for both the bayesian and
+functions we need to solve problems for both the Bayesian and
 frequentist Navy Captains.
 
 ```{code-cell} python3

lectures/odu.md

@@ -445,7 +445,7 @@ plt.show()
 ```
 
 The results fit well with our intuition from section [looking
-forward](#looking-forward).
+forward](#Looking-Forward).
 
 - The black line in the figure above corresponds to the function
   $\bar w(\pi)$ introduced there.
@@ -1057,10 +1057,10 @@ $f$?
 
 Two countervailing effects are at work.
 
-- if f generates successive wage offers, then $w$ is more likely to be low, but
+- if $f$ generates successive wage offers, then $w$ is more likely to be low, but
   $\pi$ is moving up toward to 1, which lowers the reservation wage,
   i.e., the worker becomes  less selective the longer he or she remains unemployed.
-- if g generates wage offers, then $w$ is more likely to be high, but
+- if $g$ generates wage offers, then $w$ is more likely to be high, but
   $\pi$ is moving downward toward 0, increasing the reservation wage, i.e., the worker becomes  more selective
   the longer he or she remains unemployed.
 

lectures/short_path.md

@@ -67,7 +67,7 @@ We wish to travel from node (vertex) A to node G at minimum cost
 * Arrows (edges) indicate the movements we can take.
 * Numbers on edges indicate the cost of traveling that edge.
 
-(Graphs such as the one above are called **weighted `directed graphs <https://en.wikipedia.org/wiki/Directed_graph>`_**.)
+(Graphs such as the one above are called weighted [directed graphs](https://en.wikipedia.org/wiki/Directed_graph).)
 
 Possible interpretations of the graph include