@@ -79,6 +79,7 @@ The following figure shows international trade in large commercial aircraft in 2
7979
8080``` {code-cell} ipython3
8181---
82+ tags: ["hide_input"]
8283mystnb:
8384 figure:
8485 caption: "Commercial Aircraft Network \n"
@@ -88,7 +89,6 @@ mystnb:
8889 classes: shadow bg-primary
8990 width: 200px
9091---
91- :tags: [hide_input]
9292
9393import matplotlib.cm as cm
9494import quantecon_book_networks
@@ -171,7 +171,7 @@ where the states are
171171Let's examine the following figure.
172172
173173``` {code-cell} ipython3
174- :tags: [hide_input]
174+ :tags: [" hide_input" ]
175175from graphviz import Digraph
176176
177177dot = Digraph(comment='Graph')
@@ -473,6 +473,7 @@ loans) between private banks, grouped by country of origin.
473473
474474``` {code-cell} ipython3
475475---
476+ tags: ["hide_cell"]
476477mystnb:
477478 figure:
478479 caption: "International Credit Network \n"
@@ -482,7 +483,6 @@ mystnb:
482483 classes: shadow bg-primary
483484 width: 200px
484485---
485- :tags: [hide_cell]
486486
487487Z = ch1_data["adjacency_matrix_2019"]["Z"]
488488Z_visual= ch1_data["adjacency_matrix_2019"]["Z_visual"]
@@ -583,6 +583,7 @@ representing edges of the induced directed graph.
583583
584584``` {code-cell} ipython3
585585---
586+ tags: ["hide_input"]
586587mystnb:
587588 figure:
588589 caption: "Weighted Poverty Trap \n"
@@ -592,7 +593,6 @@ mystnb:
592593 classes: shadow bg-primary
593594 width: 200px
594595---
595- :tags: [hide_input]
596596
597597graph3 = Digraph(comment='Graph')
598598
@@ -896,7 +896,7 @@ A = np.array([[0,0.7,0.3], #adjacency matrix A
896896```
897897
898898``` {code-cell} ipython3
899- :tags: [hide_cell]
899+ :tags: [" hide_cell" ]
900900def is_irreducible(P):
901901 n = len(P)
902902 result = np.zeros((n, n))
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