[all] Adopt standard figsize for quantecon-book-theme

lectures/aiyagari.md

@@ -55,10 +55,11 @@ The Aiyagari model has been used to investigate many topics, including
 Let's start with some imports:
 
 ```{code-cell} ipython
+%matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
-import matplotlib.pyplot as plt
-%matplotlib inline
 from quantecon.markov import DiscreteDP
 from numba import jit
 ```

lectures/ar1_processes.md

@@ -47,8 +47,9 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import numpy as np
-import matplotlib.pyplot as plt
 %matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 ```
 
 ## The AR(1) Model

lectures/cake_eating_numerical.md

@@ -46,10 +46,10 @@ accuracy of alternative numerical methods.
 We will use the following imports:
 
 ```{code-cell} ipython
-import numpy as np
-import matplotlib.pyplot as plt
 %matplotlib inline
-
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
+import numpy as np
 from interpolation import interp
 from scipy.optimize import minimize_scalar, bisect
 ```

lectures/cake_eating_problem.md

@@ -40,9 +40,10 @@ Readers might find it helpful to review the following lectures before reading th
 In what follows, we require the following imports:
 
 ```{code-cell} ipython
-import numpy as np
-import matplotlib.pyplot as plt
 %matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
+import numpy as np
 ```
 
 ## The Model

lectures/career.md

@@ -47,10 +47,11 @@ This exposition draws on the presentation in {cite}`Ljungqvist2012`, section 6.5
 We begin with some imports:
 
 ```{code-cell} ipython
+%matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
-import matplotlib.pyplot as plt
-%matplotlib inline
 from numba import njit, prange
 from quantecon.distributions import BetaBinomial
 from scipy.special import binom, beta

lectures/cass_koopmans_1.md

@@ -54,11 +54,12 @@ The lecture uses important ideas including
 Let's start with some standard imports:
 
 ```{code-cell} ipython
+%matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 from numba import njit, float64
 from numba.experimental import jitclass
 import numpy as np
-import matplotlib.pyplot as plt
-%matplotlib inline
 ```
 
 ## The Model

lectures/cass_koopmans_2.md

@@ -66,11 +66,12 @@ The present lecture uses  additional  ideas including
 Let's start with some standard imports:
 
 ```{code-cell} ipython
+%matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 from numba import njit, float64
 from numba.experimental import jitclass
 import numpy as np
-import matplotlib.pyplot as plt
-%matplotlib inline
 ```
 
 ## Review of Cass-Koopmans Model

lectures/coleman_policy_iter.md

@@ -61,11 +61,11 @@ iteration can be further adjusted to obtain even more efficiency.
 Let's start with some imports:
 
 ```{code-cell} ipython
+%matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
-import matplotlib.pyplot as plt
-%matplotlib inline
-
 from interpolation import interp
 from quantecon.optimize import brentq
 from numba import njit, float64

lectures/complex_and_trig.md

@@ -98,9 +98,10 @@ $$
 We'll need the following imports:
 
 ```{code-cell} ipython
-import numpy as np
-import matplotlib.pyplot as plt
 %matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
+import numpy as np
 from sympy import *
 ```
 

lectures/egm_policy_iter.md

@@ -51,14 +51,15 @@ The original reference is {cite}`Carroll2006`.
 Let's start with some standard imports:
 
 ```{code-cell} ipython
+%matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
 from interpolation import interp
 from numba import njit, float64
 from numba.experimental import jitclass
 from quantecon.optimize import brentq
-import matplotlib.pyplot as plt
-%matplotlib inline
 ```
 
 ## Key Idea

lectures/exchangeable.md

@@ -66,13 +66,14 @@ Let’s start with some imports:
 ---
 tags: [hide-output]
 ---
+%matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 from numba import njit, vectorize
 from math import gamma
 import scipy.optimize as op
 from scipy.integrate import quad
 import numpy as np
-import matplotlib.pyplot as plt
-%matplotlib inline
 ```
 
 ## Independently and Identically Distributed

lectures/finite_markov.md

@@ -52,11 +52,12 @@ Prerequisite knowledge is basic probability and linear algebra.
 Let's start with some standard imports:
 
 ```{code-cell} ipython
+%matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import quantecon as qe
 import numpy as np
 from mpl_toolkits.mplot3d import Axes3D
-import matplotlib.pyplot as plt
-%matplotlib inline
 ```
 
 ## Definitions

lectures/heavy_tails.md

@@ -81,10 +81,11 @@ key ideas.
 Let's start with some imports:
 
 ```{code-cell} ipython
+%matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
-import matplotlib.pyplot as plt
-%matplotlib inline
 ```
 
 The following two lines can be added to avoid an annoying FutureWarning, and prevent a specific compatibility issue between pandas and matplotlib from causing problems down the line:

lectures/ifp.md

@@ -59,13 +59,14 @@ Time iteration is globally convergent under mild assumptions, even when utility
 We'll need the following imports:
 
 ```{code-cell} ipython
+%matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from quantecon.optimize import brent_max, brentq
 from interpolation import interp
 from numba import njit, float64
 from numba.experimental import jitclass
-import matplotlib.pyplot as plt
-%matplotlib inline
 from quantecon import MarkovChain
 ```
 

lectures/ifp_advanced.md

@@ -56,13 +56,14 @@ endogenous grid method to solve the model quickly and accurately.
 We require the following imports:
 
 ```{code-cell} ipython
+%matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from quantecon.optimize import brent_max, brentq
 from interpolation import interp
 from numba import njit, float64
 from numba.experimental import jitclass
-import matplotlib.pyplot as plt
-%matplotlib inline
 from quantecon import MarkovChain
 ```
 

lectures/inventory_dynamics.md

@@ -47,10 +47,10 @@ While our Markov environment and many of the concepts we consider are related to
 Let's start with some imports
 
 ```{code-cell} ipython3
-import numpy as np
-import matplotlib.pyplot as plt
 %matplotlib inline
-
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
+import numpy as np
 from numba import njit, float64, prange
 from numba.experimental import jitclass
 ```

lectures/jv.md

@@ -46,12 +46,13 @@ In this section, we solve a simple on-the-job search model
 Let's start with some imports:
 
 ```{code-cell} ipython
+%matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import scipy.stats as stats
 from interpolation import interp
 from numba import njit, prange
-import matplotlib.pyplot as plt
-%matplotlib inline
 from math import gamma
 ```
 

lectures/kalman.md

@@ -55,11 +55,12 @@ Required knowledge: Familiarity with matrix manipulations, multivariate normal d
 We'll need the following imports:
 
 ```{code-cell} ipython
+%matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 from scipy import linalg
 import numpy as np
 import matplotlib.cm as cm
-import matplotlib.pyplot as plt
-%matplotlib inline
 from quantecon import Kalman, LinearStateSpace
 from scipy.stats import norm
 from scipy.integrate import quad

lectures/kesten_processes.md

@@ -54,10 +54,10 @@ We will discuss these issues as we go along.
 Let's start with some imports:
 
 ```{code-cell} ipython
-import numpy as np
-import matplotlib.pyplot as plt
 %matplotlib inline
-
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
+import numpy as np
 import quantecon as qe
 ```
 

lectures/lake_model.md

@@ -68,10 +68,11 @@ These concepts will help us build an equilibrium model of ex-ante homogeneous wo
 Let's start with some imports:
 
 ```{code-cell} ipython
+%matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from quantecon import MarkovChain
-import matplotlib.pyplot as plt
-%matplotlib inline
 from scipy.stats import norm
 from scipy.optimize import brentq
 from quantecon.distributions import BetaBinomial

lectures/likelihood_bayes.md

@@ -25,11 +25,12 @@ kernelspec:
 ```
 
 ```{code-cell} ipython
-import numpy as np
+%matplotlib inline
 import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
+import numpy as np
 from numba import vectorize, njit
 from math import gamma
-%matplotlib inline
 ```
 
 ## Overview

lectures/likelihood_ratio_process.md

@@ -25,11 +25,12 @@ kernelspec:
 ```
 
 ```{code-cell} ipython
-import numpy as np
+%matplotlib inline
 import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
+import numpy as np
 from numba import vectorize, njit
 from math import gamma
-%matplotlib inline
 from scipy.integrate import quad
 ```
 

lectures/linear_algebra.md

@@ -73,9 +73,10 @@ material that will be used in applications as we go along.
 Let's start with some imports:
 
 ```{code-cell} ipython
-import numpy as np
-import matplotlib.pyplot as plt
 %matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
+import numpy as np
 from matplotlib import cm
 from mpl_toolkits.mplot3d import Axes3D
 from scipy.interpolate import interp2d

lectures/linear_models.md

@@ -66,9 +66,10 @@ Its many applications include:
 Let's start with some imports:
 
 ```{code-cell} ipython
-import numpy as np
-import matplotlib.pyplot as plt
 %matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
+import numpy as np
 from quantecon import LinearStateSpace
 from scipy.stats import norm
 import random

lectures/lln_clt.md

@@ -51,10 +51,11 @@ Some of these extensions are presented as exercises.
 We'll need the following imports:
 
 ```{code-cell} ipython
+%matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import random
 import numpy as np
-import matplotlib.pyplot as plt
-%matplotlib inline
 from scipy.stats import t, beta, lognorm, expon, gamma, uniform, cauchy
 from scipy.stats import gaussian_kde, poisson, binom, norm, chi2
 from mpl_toolkits.mplot3d import Axes3D

lectures/lq_inventories.md

@@ -203,10 +203,11 @@ Here is code for computing an optimal decision rule and for analyzing
 its consequences.
 
 ```{code-cell} ipython
+%matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
-import matplotlib.pyplot as plt
-%matplotlib inline
 ```
 
 ```{code-cell} python3

lectures/lqcontrol.md

@@ -70,9 +70,10 @@ In order to focus on computation, we leave longer proofs to these sources (while
 Let's start with some imports:
 
 ```{code-cell} ipython
-import numpy as np
-import matplotlib.pyplot as plt
 %matplotlib inline
+import matplotlib.pyplot as plt
+plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
+import numpy as np
 from quantecon import LQ
 ```