[style] Remove local figsize adjustments from all lecture files

lectures/aiyagari.md

@@ -57,7 +57,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython3
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from quantecon.markov import DiscreteDP
 from numba import jit

lectures/cake_eating_problem.md

@@ -41,7 +41,6 @@ In what follows, we require the following imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 ```
 

lectures/career.md

@@ -48,7 +48,6 @@ We begin with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
 from numba import jit, prange

lectures/cass_koopmans_2.md

@@ -67,7 +67,6 @@ Let's start with some standard imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 from numba import jit, float64
 from numba.experimental import jitclass
 import numpy as np

lectures/exchangeable.md

@@ -71,7 +71,6 @@ Let’s start with some imports:
 tags: [hide-output]
 ---
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 from numba import jit, vectorize
 from math import gamma
 import scipy.optimize as op

lectures/finite_markov.md

@@ -53,7 +53,6 @@ Let's start with some standard imports:
 
 ```{code-cell} ipython
 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

lectures/inventory_dynamics.md

@@ -48,7 +48,6 @@ Let's start with some imports
 
 ```{code-cell} ipython3
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numba import jit, float64, prange
 from numba.experimental import jitclass

lectures/lake_model.md

@@ -69,7 +69,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from quantecon import MarkovChain
 from scipy.stats import norm

lectures/likelihood_bayes.md

@@ -52,7 +52,6 @@ We'll begin by loading some Python modules.
 :hide-output: false
 
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numba import vectorize, jit, prange
 from math import gamma

lectures/linear_algebra.md

@@ -74,7 +74,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 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

lectures/lln_clt.md

@@ -52,7 +52,6 @@ We'll need the following imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import random
 import numpy as np
 from scipy.stats import t, beta, lognorm, expon, gamma, uniform

lectures/lq_inventories.md

@@ -204,7 +204,6 @@ its consequences.
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
 ```

lectures/lqcontrol.md

@@ -71,7 +71,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from quantecon import LQ
 ```

lectures/markov_asset.md

@@ -73,7 +73,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
 from numpy.linalg import eigvals, solve

lectures/mccall_model.md

@@ -61,7 +61,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numba import jit, float64
 from numba.experimental import jitclass

lectures/mix_model.md

@@ -124,7 +124,6 @@ As usual, we'll start by importing some Python tools.
 :hide-output: false
 
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numba import vectorize, jit
 from math import gamma

lectures/mle.md

@@ -44,7 +44,6 @@ We'll require the following imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numpy import exp
 from scipy.special import factorial, gammaln

lectures/multi_hyper.md

@@ -112,7 +112,6 @@ Let's start with some imports.
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from scipy.special import comb
 from scipy.stats import normaltest

lectures/multivariate_normal.md

@@ -62,7 +62,6 @@ We  use the following imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numba import jit
 import statsmodels.api as sm

lectures/newton_method.md

@@ -90,8 +90,6 @@ from scipy.optimize import root
 from autograd import jacobian
 # Thinly-wrapped numpy to enable automatic differentiation
 import autograd.numpy as np
-
-plt.rcParams["figure.figsize"] = (10, 5.7)
 ```
 
 ## Fixed Point Computation Using Newton's Method

lectures/ols.md

@@ -59,7 +59,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import pandas as pd
 import statsmodels.api as sm

lectures/optgrowth.md

@@ -61,7 +61,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from scipy.interpolate import interp1d
 from scipy.optimize import minimize_scalar

lectures/perm_income.md

@@ -48,7 +48,6 @@ Let's start with some imports
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import random
 from numba import jit

lectures/rational_expectations.md

@@ -68,7 +68,6 @@ Let's start with some standard imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 ```
 

lectures/samuelson.md

@@ -48,7 +48,6 @@ Let's start with some standard imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 ```
 

lectures/sir_model.md

@@ -52,7 +52,6 @@ We will use the following standard imports:
 
 ```{code-cell} ipython3
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numpy import exp
 ```

lectures/uncertainty_traps.md

@@ -52,7 +52,6 @@ Let's start with some standard imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 ```
 

lectures/wealth_dynamics.md

@@ -77,7 +77,6 @@ We will use the following imports.
 
 ```{code-cell} ipython3
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
 from numba import jit, float64, prange