deploy: da95c81

_sources/ch_fpms.md

@@ -19,7 +19,7 @@ kernelspec:
 ---
 tags: [hide-output]
 ---
-pip install --upgrade quantecon_book_networks
+! pip install --upgrade quantecon_book_networks
 ```
 
 We begin with some imports

_sources/ch_intro.md

@@ -19,7 +19,7 @@ kernelspec:
 ---
 tags: [hide-output]
 ---
-pip install --upgrade quantecon_book_networks kaleido
+! pip install --upgrade quantecon_book_networks kaleido
 ```
 
 We begin by importing the `quantecon` package as well as some functions and data that have been packaged for release with this text.
@@ -31,7 +31,8 @@ import quantecon_book_networks.input_output as qbn_io
 import quantecon_book_networks.data as qbn_data
 import quantecon_book_networks.plotting as qbn_plot
 ch1_data = qbn_data.introduction()
-export_figures = False
+default_figsize = [6, 4]
+export_figures = True
 ```
 
 Next we import some common python libraries.
@@ -315,7 +316,7 @@ def Ge(x):
 We then plot our distribution on a log-log scale.
 
 ```{code-cell} ipython3
-fig, ax = plt.subplots()
+fig, ax = plt.subplots(figsize=default_figsize)
 
 ax.plot(np.log(x), np.log(Gp(x)), label="Pareto")
 ax.plot(np.log(x), np.log(Ge(x)), label="Exponential")
@@ -360,7 +361,7 @@ b, a = results.params
 Finally we produce our plot.
 
 ```{code-cell} ipython3
-fig, ax = plt.subplots(figsize=(6.4, 3.5))
+fig, ax = plt.subplots(figsize=[7.3,4])
 
 ax.scatter(x, y, alpha=0.3, label="firm size (market value)")
 ax.plot(x, x * a + b, 'k-', alpha=0.6, label=f"slope = ${a: 1.2f}$")
@@ -402,7 +403,7 @@ x_grid = np.linspace(1, 10, 200)
 Then we can produce our plot
 
 ```{code-cell} ipython3
-fig, ax = plt.subplots()
+fig, ax = plt.subplots(figsize=default_figsize)
 ax.plot(k_grid, z(k_grid), '-o', label='zeta distribution with $\gamma=2$')
 ax.plot(x_grid, p(x_grid), label='density of Pareto with tail index $\\alpha$')
 ax.legend(fontsize=12)
@@ -466,11 +467,8 @@ G.add_edge(3, 2)
 list(nx.strongly_connected_components(G))
 ```
 
-Like `NetworkX`, the QuantEcon Python library `quantecon` supplies a graph
-object that implements certain graph-theoretic algorithms. 
-
-The set of available algorithms is more limited but each one is faster, accelerated by
-just-in-time compilation. 
+Like `NetworkX`, the Python library `quantecon` 
+provides access to some graph-theoretic algorithms. 
 
 In the case of QuantEcon's `DiGraph` object, an instance is created via the adjacency matrix.
 
@@ -683,7 +681,7 @@ def plot_degree_dist(G, ax, loglog=True, label=None):
 ```
 
 ```{code-cell} ipython3
-fig, ax = plt.subplots()
+fig, ax = plt.subplots(figsize=default_figsize)
 
 plot_degree_dist(DG, ax, loglog=False, label='degree distribution')
 

_sources/ch_mcs.md

@@ -19,7 +19,7 @@ kernelspec:
 ---
 tags: [hide-output]
 ---
-pip install --upgrade quantecon_book_networks
+! pip install --upgrade quantecon_book_networks
 ```
 
 We begin with some imports.
@@ -31,7 +31,7 @@ import quantecon_book_networks.input_output as qbn_io
 import quantecon_book_networks.plotting as qbn_plt
 import quantecon_book_networks.data as qbn_data
 ch4_data = qbn_data.markov_chains_and_networks()
-export_figures = False
+export_figures = True
 ```
 
 ```{code-cell} ipython3
@@ -68,7 +68,7 @@ P_Q = [
     [0,    0,    0,    0.01, 0.99]
 ]
 P_Q = np.array(P_Q)
-codes_Q =  ( '1','2','3','4','5')
+codes_Q =  ('1', '2', '3', '4', '5')
 ```
 
 Second, [Benhabib et al. (2015)](https://www.economicdynamics.org/meetpapers/2015/paper_364.pdf) estimate the following transition matrix for intergenerational social mobility.
@@ -90,7 +90,7 @@ P_B = [
     ]
 
 P_B = np.array(P_B)
-codes_B =  ( '1','2','3','4','5','6','7','8')
+codes_B =  ('1', '2', '3', '4', '5', '6', '7', '8')
 ```
 
 
@@ -176,17 +176,21 @@ minimum initial value and one at the maximum.
 def sim_fig(ax, mc, T=100, seed=14, title=None):
     X1 = mc.simulate(T, init=1, random_state=seed)
     X2 = mc.simulate(T, init=max(mc.state_values), random_state=seed+1)
-    ax.plot(X1, label="low initial state")
-    ax.plot(X2, label="high initial state")
+    ax.plot(X1)
+    ax.plot(X2)
+    ax.set_xlabel("time")
+    ax.set_ylabel("state")
     ax.set_title(title, fontsize=12)
 ```
 
 Finally, we produce the figure.
 
 ```{code-cell}
-fig, axes = plt.subplots(2, 1)
+fig, axes = plt.subplots(2, 1, figsize=(6, 4))
+ax = axes[0]
 sim_fig(axes[0], mc_B, title="$P_B$")
 sim_fig(axes[1], mc_Q, title="$P_Q$")
+axes[1].set_yticks((1, 2, 3, 4, 5))
 
 plt.tight_layout()
 if export_figures:
@@ -256,12 +260,19 @@ Now, calculate the true 2019 distribution.
 Finally we produce the plot. 
 
 ```{code-cell}
-states = np.arange(1, 6)
+states = np.arange(0, 5)
+ticks = range(5)
+codes_S = ('1', '2', '3', '4', '5')
 
-fig, ax = plt.subplots()
+fig, ax = plt.subplots(figsize=(6, 4))
 width = 0.4
 ax.plot(states, ψ_2019_predicted, '-o', alpha=0.7, label='predicted')
 ax.plot(states, ψ_2019, '-o', alpha=0.7, label='realized')
+ax.set_xlabel("state")
+ax.set_ylabel("probability")
+ax.set_yticks((0.15, 0.2, 0.25, 0.3))
+ax.set_xticks(ticks)
+ax.set_xticklabels(codes_S)
 
 ax.legend(loc='upper center', fontsize=12)
 if export_figures:
@@ -364,6 +375,7 @@ def transition(P, n, ax=None):
     ax.set(ylim=(0, 0.25), 
            xticks=((1, nstates)))
     ax.set_title(f"t = {n}")
+    ax.set_xlabel("Quantile")
     
     return ax
 ```
@@ -372,12 +384,11 @@ We now generate the marginal distributions after 0, 1, 2, and 100 iterations for
 
 ```{code-cell}
 ns = (0, 1, 2, 100)
-fig, axes = plt.subplots(1, len(ns))
+fig, axes = plt.subplots(1, len(ns), figsize=(6, 4))
 
 for n, ax in zip(ns, axes):
     ax = transition(P_B, n, ax=ax)
     
-axes[-1].set_xlabel("Quantile")
 
 plt.tight_layout()
 if export_figures:

_sources/ch_opt.md

@@ -19,7 +19,7 @@ kernelspec:
 ---
 tags: [hide-output]
 ---
-pip install --upgrade quantecon_book_networks
+! pip install --upgrade quantecon_book_networks
 ```
 
 We begin with some imports
@@ -31,7 +31,7 @@ import quantecon_book_networks.input_output as qbn_io
 import quantecon_book_networks.plotting as qbn_plt
 import quantecon_book_networks.data as qbn_data
 ch3_data = qbn_data.optimal_flows()
-export_figures = False
+export_figures = True
 ```
 
 ```{code-cell}
@@ -64,7 +64,7 @@ bc_dict = nx.betweenness_centrality(G)
 And we produce the plot.
 
 ```{code-cell}
-fig, ax = plt.subplots(figsize=(10, 10))
+fig, ax = plt.subplots(figsize=(9.4, 9.4))
 
 plt.axis("off")
 nx.draw_networkx(
@@ -111,8 +111,8 @@ plt.rcParams['font.size'] = '14'
 
 ax.plot(np.linspace(-1, 17.5, 100), 6-0.4*np.linspace(-1, 17.5, 100))
 ax.plot(np.linspace(-1, 5.5, 100), 10-2*np.linspace(-1, 5.5, 100))
-ax.text(1.5, 8, "$2q_1 + 5q_2 \leq 30$")
-ax.text(10, 2.5, "$4q_1 + 2q_2 \leq 20$")
+ax.text(10, 2.5, "$2q_1 + 5q_2 \leq 30$")
+ax.text(1.5, 8, "$4q_1 + 2q_2 \leq 20$")
 ax.text(-2, 2, "$q_2 \geq 0$")
 ax.text(2.5, -0.7, "$q_1 \geq 0$")
 

_sources/ch_opt_julia.md

@@ -56,12 +56,14 @@ $T^2_q$, $T^3_q$ and plot them. By $T^3_q$ has already converged on $q^∗$.
 
 ```{code-cell}
 using PyPlot
-export_figures = false
-fig, ax = plt.subplots()
+export_figures = true
+fig, ax = plt.subplots(figsize=[6, 4])
 
 n = 7
 q = zeros(n)
 ax.plot(1:n, q)
+ax.set_xlabel("cost-to-go")
+ax.set_ylabel("nodes")
 
 for i in 1:3
     new_q = T(q)

_sources/ch_production.md

@@ -19,7 +19,7 @@ kernelspec:
 ---
 tags: [hide-output]
 ---
-pip install --upgrade quantecon_book_networks
+! pip install --upgrade quantecon_book_networks
 ```
 
 We begin with some imports.
@@ -31,7 +31,8 @@ import quantecon_book_networks.input_output as qbn_io
 import quantecon_book_networks.plotting as qbn_plt
 import quantecon_book_networks.data as qbn_data
 ch2_data = qbn_data.production()
-export_figures = False
+default_figsize = [6, 4]
+export_figures = True
 ```
 
 ```{code-cell} ipython3
@@ -104,9 +105,10 @@ fig, ax = plt.subplots(figsize=(8, 10))
 plt.axis("off")
 color_list = qbn_io.colorise_weights(centrality, beta=False)
 # Remove self-loops
-for i in range(A.shape[0]):
-    A[i][i] = 0
-qbn_plt.plot_graph(A, X, ax, codes, 
+A1 = A.copy()
+for i in range(A1.shape[0]):
+    A1[i][i] = 0
+qbn_plt.plot_graph(A1, X, ax, codes, 
               layout_type='spring',
               layout_seed=5432167,
               tol=0.0,
@@ -122,7 +124,7 @@ plt.show()
 Now we plot a bar chart of hub-based eigenvector centrality by sector.
 
 ```{code-cell} ipython3
-fig, ax = plt.subplots()
+fig, ax = plt.subplots(figsize=default_figsize)
 ax.bar(codes, centrality, color=color_list, alpha=0.6)
 ax.set_ylabel("eigenvector centrality", fontsize=12)
 if export_figures:
@@ -143,7 +145,7 @@ omult = qbn_io.katz_centrality(A, authority=True)
 ```
 
 ```{code-cell} ipython3
-fig, ax = plt.subplots()
+fig, ax = plt.subplots(figsize=default_figsize)
 omult_color_list = qbn_io.colorise_weights(omult,beta=False)
 ax.bar(codes, omult, color=omult_color_list, alpha=0.6)
 ax.set_ylabel("Output multipliers", fontsize=12)
@@ -188,7 +190,7 @@ plt.show()
 Here we produce a barplot of upstreamness.
 
 ```{code-cell} ipython3
-fig, ax = plt.subplots()
+fig, ax = plt.subplots(figsize=default_figsize)
 ax.bar(codes, upstreamness, color=upstreamness_color_list, alpha=0.6)
 ax.set_ylabel("upstreamness", fontsize=12)
 if export_figures:
@@ -205,7 +207,7 @@ kcentral = qbn_io.katz_centrality(A)
 ```
 
 ```{code-cell} ipython3
-fig, ax = plt.subplots()
+fig, ax = plt.subplots(figsize=default_figsize)
 kcentral_color_list = qbn_io.colorise_weights(kcentral,beta=False)
 ax.bar(codes, kcentral, color=kcentral_color_list, alpha=0.6)
 ax.set_ylabel("Katz hub centrality", fontsize=12)
@@ -264,11 +266,12 @@ d[6] = 1  # positive shock to agriculture
 Now we simulate the demand shock propagating through the economy.
 
 ```{code-cell} ipython3
-sim_length = 6
+sim_length = 11
 x = d
 x_vecs = []
 for i in range(sim_length):
-    x_vecs.append(x)
+    if i % 2 ==0:
+        x_vecs.append(x)
     x = A @ x
 ```
 
@@ -279,8 +282,13 @@ fig, axes = plt.subplots(3, 2, figsize=(8, 10))
 axes = axes.flatten()
 
 for ax, x_vec, i in zip(axes, x_vecs, range(sim_length)):
-    ax.set_title(f"round {i}")
+    if i % 2 != 0:
+        pass
+    ax.set_title(f"round {i*2}")
     x_vec_cols = qbn_io.colorise_weights(x_vec,beta=False)
+    # remove self-loops
+    for i in range(len(A)):
+        A[i][i] = 0
     qbn_plt.plot_graph(A, X, ax, codes,
                   layout_type='spring',
                   layout_seed=342156,
@@ -356,7 +364,7 @@ color_list_114 = qbn_io.colorise_weights(centrality_114,beta=False)
 Finally we produce the plot.
 
 ```{code-cell} ipython3
-fig, ax = plt.subplots(figsize=(10, 12))
+fig, ax = plt.subplots(figsize=(11, 13.2))
 plt.axis("off")
 # Remove self-loops
 for i in range(A_114.shape[0]):

ch_fpms.html

@@ -222,40 +222,39 @@
 <h1>Chapter 5 - Nonlinear Interactions (Python Code)<a class="headerlink" href="#chapter-5-nonlinear-interactions-python-code" title="Permalink to this headline">¶</a></h1>
 <div class="cell tag_hide-output docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="o">--</span><span class="n">upgrade</span> <span class="n">quantecon_book_networks</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span> pip install --upgrade quantecon_book_networks
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Requirement already satisfied: quantecon_book_networks in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (0.5.1)
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Requirement already satisfied: numpy in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from quantecon_book_networks) (1.22.4)
-Requirement already satisfied: scipy in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from quantecon_book_networks) (1.7.1)
-Requirement already satisfied: networkx in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from quantecon_book_networks) (2.6.3)
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Requirement already satisfied: networkx in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from quantecon_book_networks) (2.6.3)
+Requirement already satisfied: pandas in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from quantecon_book_networks) (1.3.4)
 Requirement already satisfied: matplotlib in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from quantecon_book_networks) (3.4.3)
+Requirement already satisfied: scipy in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from quantecon_book_networks) (1.7.1)
+Requirement already satisfied: numpy in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from quantecon_book_networks) (1.22.4)
 Requirement already satisfied: pandas-datareader in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from quantecon_book_networks) (0.10.0)
-Requirement already satisfied: pandas in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from quantecon_book_networks) (1.3.4)
 Requirement already satisfied: pillow&gt;=6.2.0 in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from matplotlib-&gt;quantecon_book_networks) (8.4.0)
 Requirement already satisfied: python-dateutil&gt;=2.7 in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from matplotlib-&gt;quantecon_book_networks) (2.8.2)
-Requirement already satisfied: pyparsing&gt;=2.2.1 in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from matplotlib-&gt;quantecon_book_networks) (3.0.4)
 Requirement already satisfied: cycler&gt;=0.10 in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from matplotlib-&gt;quantecon_book_networks) (0.10.0)
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Requirement already satisfied: pyparsing&gt;=2.2.1 in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from matplotlib-&gt;quantecon_book_networks) (3.0.4)
 Requirement already satisfied: kiwisolver&gt;=1.0.1 in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from matplotlib-&gt;quantecon_book_networks) (1.3.1)
 Requirement already satisfied: six in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from cycler&gt;=0.10-&gt;matplotlib-&gt;quantecon_book_networks) (1.16.0)
+Requirement already satisfied: pytz&gt;=2017.3 in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from pandas-&gt;quantecon_book_networks) (2021.3)
+Requirement already satisfied: requests&gt;=2.19.0 in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from pandas-datareader-&gt;quantecon_book_networks) (2.26.0)
+Requirement already satisfied: lxml in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from pandas-datareader-&gt;quantecon_book_networks) (4.6.3)
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Requirement already satisfied: pytz&gt;=2017.3 in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from pandas-&gt;quantecon_book_networks) (2021.3)
-Requirement already satisfied: lxml in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from pandas-datareader-&gt;quantecon_book_networks) (4.6.3)
-Requirement already satisfied: requests&gt;=2.19.0 in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from pandas-datareader-&gt;quantecon_book_networks) (2.26.0)
-Requirement already satisfied: certifi&gt;=2017.4.17 in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from requests&gt;=2.19.0-&gt;pandas-datareader-&gt;quantecon_book_networks) (2021.10.8)
-Requirement already satisfied: urllib3&lt;1.27,&gt;=1.21.1 in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from requests&gt;=2.19.0-&gt;pandas-datareader-&gt;quantecon_book_networks) (1.26.7)
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Requirement already satisfied: urllib3&lt;1.27,&gt;=1.21.1 in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from requests&gt;=2.19.0-&gt;pandas-datareader-&gt;quantecon_book_networks) (1.26.7)
 Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from requests&gt;=2.19.0-&gt;pandas-datareader-&gt;quantecon_book_networks) (2.0.4)
+Requirement already satisfied: certifi&gt;=2017.4.17 in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from requests&gt;=2.19.0-&gt;pandas-datareader-&gt;quantecon_book_networks) (2021.10.8)
 Requirement already satisfied: idna&lt;4,&gt;=2.5 in /usr/share/miniconda3/envs/networks/lib/python3.8/site-packages (from requests&gt;=2.19.0-&gt;pandas-datareader-&gt;quantecon_book_networks) (3.2)
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Note: you may need to restart the kernel to use updated packages.
-</pre></div>
-</div>
 </div>
 </div>
 <p>We begin with some imports</p>