MAINT: remove %matplotlib inline and contents directive (#161)

lectures/BCG_complete_mkts.md

@@ -20,10 +20,6 @@ kernelspec:
 
 # Irrelevance of Capital Structures with Complete Markets
 
-```{contents} Contents
-:depth: 2
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython
@@ -872,7 +868,6 @@ import matplotlib.pyplot as plt
 from scipy.stats import norm
 from numba import njit, prange
 from quantecon.optimize import root_finding
-%matplotlib inline
 ```
 
 ```{code-cell} python3

lectures/BCG_incomplete_mkts.md

@@ -22,10 +22,6 @@ kernelspec:
 
 # Equilibrium Capital Structures with Incomplete Markets
 
-```{contents} Contents
-:depth: 2
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython3

lectures/additive_functionals.md

@@ -25,10 +25,6 @@ kernelspec:
 ```{index} single: Models; Additive functionals
 ```
 
-```{contents} Contents
-:depth: 2
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython3
@@ -78,7 +74,6 @@ import numpy as np
 import scipy.linalg as la
 import quantecon as qe
 import matplotlib.pyplot as plt
-%matplotlib inline
 from scipy.stats import norm, lognorm
 ```
 

lectures/amss.md

@@ -20,10 +20,6 @@ kernelspec:
 
 # Optimal Taxation without State-Contingent Debt
 
-```{contents} Contents
-:depth: 2
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython

lectures/amss2.md

@@ -20,10 +20,6 @@ kernelspec:
 
 # Fluctuating Interest Rates Deliver Fiscal Insurance
 
-```{contents} Contents
-:depth: 2
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython
@@ -89,7 +85,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-%matplotlib inline
 from scipy.optimize import fsolve, fmin
 ```
 

lectures/amss3.md

@@ -20,10 +20,6 @@ kernelspec:
 
 # Fiscal Risk and Government Debt
 
-```{contents} Contents
-:depth: 2
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython
@@ -74,7 +70,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-%matplotlib inline
 from scipy.optimize import minimize
 ```
 

lectures/arellano.md

@@ -20,10 +20,6 @@ kernelspec:
 
 # Default Risk and Income Fluctuations
 
-```{contents} Contents
-:depth: 2
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} python
@@ -78,9 +74,7 @@ Let's start with some imports:
 import matplotlib.pyplot as plt
 import numpy as np
 import quantecon as qe
-
 from numba import njit, prange
-%matplotlib inline
 ```
 
 ## Structure

lectures/arma.md

@@ -20,10 +20,6 @@ kernelspec:
 
 # {index}`Covariance Stationary Processes <single: Covariance Stationary Processes>`
 
-```{contents} Contents
-:depth: 2
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython
@@ -95,7 +91,6 @@ Let's start with some imports:
 ```{code-cell} ipython
 import numpy as np
 import matplotlib.pyplot as plt
-%matplotlib inline
 import quantecon as qe
 ```
 

lectures/asset_pricing_lph.md

@@ -17,10 +17,6 @@ kernelspec:
 ```{index} single: Elementary Asset Pricing
 ```
 
-```{contents} Contents
-:depth: 2
-```
-
 ## Overview
 
 This lecture is about  some implications of  asset-pricing theories that are based on the equation
@@ -356,7 +352,6 @@ In drawing a frontier, we'll set $\sigma(m) = .25$ and $E m = .99$, values rough
 ```{code-cell} ipython3
 import matplotlib.pyplot as plt
 import numpy as np
-%matplotlib inline
 
 # Define the function to plot
 def y(x, alpha, beta):
@@ -590,7 +585,6 @@ Let's start with some imports.
 import numpy as np
 import statsmodels.api as sm
 import matplotlib.pyplot as plt
-%matplotlib inline
 ```
 
 Lots of our calculations will involve computing population and sample OLS regressions.

lectures/black_litterman.md

@@ -23,10 +23,6 @@ kernelspec:
 
 # Two Modifications of Mean-Variance Portfolio Theory
 
-```{contents} Contents
-:depth: 2
-```
-
 ## Overview
 
 This lecture describes extensions to the classical mean-variance portfolio theory summarized in our lecture [Elementary Asset Pricing Theory](https://python-advanced.quantecon.org/asset_pricing_lph.html).
@@ -87,7 +83,6 @@ Let's start with some imports:
 import numpy as np
 import scipy.stats as stat
 import matplotlib.pyplot as plt
-%matplotlib inline
 from ipywidgets import interact, FloatSlider
 ```
 

lectures/calvo.md

@@ -23,10 +23,6 @@ kernelspec:
 ```{index} single: Models; Additive functionals
 ```
 
-```{contents} Contents
-:depth: 2
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython
@@ -81,7 +77,6 @@ We'll start with some imports:
 import numpy as np
 from quantecon import LQ
 import matplotlib.pyplot as plt
-%matplotlib inline
 ```
 
 ## The Model

lectures/cattle_cycles.md

@@ -23,10 +23,6 @@ kernelspec:
 
 # Cattle Cycles
 
-```{contents} Contents
-:depth: 2
-```
-
 This is another member of a suite of lectures that use the quantecon DLE class to instantiate models within the
 {cite}`HS2013` class of models described in detail in {doc}`Recursive Models of Dynamic Linear Economies <hs_recursive_models>`.
 
@@ -53,7 +49,6 @@ import matplotlib.pyplot as plt
 from collections import namedtuple
 from quantecon import DLE
 from math import sqrt
-%matplotlib inline
 ```
 
 ## The Model

lectures/chang_credible.md

@@ -20,10 +20,6 @@ kernelspec:
 
 # Credible Government Policies in a Model of Chang
 
-```{contents} Contents
-:depth: 2
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython
@@ -88,7 +84,6 @@ Let's start with some standard imports:
 import numpy as np
 import polytope
 import matplotlib.pyplot as plt
-%matplotlib inline
 ```
 
 ## The Setting

lectures/chang_ramsey.md

@@ -20,10 +20,6 @@ kernelspec:
 
 # Competitive Equilibria of a Model of Chang
 
-```{contents} Contents
-:depth: 2
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython
@@ -76,7 +72,6 @@ We'll start with some standard imports:
 import numpy as np
 import polytope
 import matplotlib.pyplot as plt
-%matplotlib inline
 ```
 
 ### The Setting

lectures/classical_filtering.md

@@ -20,10 +20,6 @@ kernelspec:
 
 # Classical Prediction and Filtering With Linear Algebra
 
-```{contents} Contents
-:depth: 2
-```
-
 ## Overview
 
 This is a sequel to the earlier lecture {doc}`Classical Control with Linear Algebra <lu_tricks>`.

lectures/coase.md

@@ -22,10 +22,6 @@ kernelspec:
 
 # {index}`Coase's Theory of the Firm <single: Coase's Theory of the Firm>`
 
-```{contents} Contents
-:depth: 2
-```
-
 ## Overview
 
 In 1937, Ronald Coase wrote a brilliant essay on the nature of the firm {cite}`coase1937nature`.

lectures/cons_news.md

@@ -20,10 +20,6 @@ kernelspec:
 
 # Information and Consumption Smoothing
 
-```{contents} Contents
-:depth: 2
-```
-
 In addition to what's in Anaconda, this lecture employs the following libraries:
 
 ```{code-cell} ipython
@@ -607,7 +603,6 @@ As usual, we start by importing packages.
 import numpy as np
 import quantecon as qe
 import matplotlib.pyplot as plt
-%matplotlib inline
 ```
 
 ```{code-cell} python3

lectures/discrete_dp.md

@@ -20,10 +20,6 @@ kernelspec:
 
 # {index}`Discrete State Dynamic Programming <single: Discrete State Dynamic Programming>`
 
-```{contents} Contents
-:depth: 2
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython
@@ -66,7 +62,6 @@ Let's start with some imports:
 ```{code-cell} ipython
 import numpy as np
 import matplotlib.pyplot as plt
-%matplotlib inline
 import quantecon as qe
 import scipy.sparse as sparse
 from quantecon import compute_fixed_point

lectures/dyn_stack.md

@@ -20,10 +20,6 @@ kernelspec:
 
 # Stackelberg Plans
 
-```{contents} Contents
-:depth: 2
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython
@@ -57,7 +53,6 @@ import numpy.linalg as la
 import quantecon as qe
 from quantecon import LQ
 import matplotlib.pyplot as plt
-%matplotlib inline
 ```
 
 ## Duopoly

lectures/estspec.md

@@ -18,15 +18,11 @@ kernelspec:
 </div>
 ```
 
-# Estimation of {index}`Spectra <single: Spectra>`
+# Estimation of Spectra
 
 ```{index} single: Spectra; Estimation
 ```
 
-```{contents} Contents
-:depth: 2
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython
@@ -59,7 +55,6 @@ Let's start with some standard imports:
 ```{code-cell} ipython
 import numpy as np
 import matplotlib.pyplot as plt
-%matplotlib inline
 from quantecon import ARMA, periodogram, ar_periodogram
 ```
 

lectures/five_preferences.md

@@ -36,8 +36,8 @@ The preference orderings are
 This labeling  scheme is taken from {cite}`HansenSargent2001`.
 
 
-Constraint and multiplier preferences express  aversion to not knowing a unique probabiity distribution
-that desribes random outcomes.
+Constraint and multiplier preferences express  aversion to not knowing a unique probability distribution
+that describes random outcomes.
 
 Expected utility, risk-sensitive, and ex post Bayesian expected utility preferences all attribute a unique known
 probability distribution to a decision maker.
@@ -59,7 +59,6 @@ We begin with some that we'll use to create some graphs.
 import numpy as np
 import matplotlib as mpl
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)
 from matplotlib import rc, cm
 from mpl_toolkits.mplot3d import Axes3D
 from scipy import optimize, stats
@@ -73,7 +72,6 @@ from numba import njit
 :tags: [hide-input]
 
 # Plotting parameters
-%matplotlib inline
 %config InlineBackend.figure_format='retina'
 
 plt.rc('text', usetex=True)