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

Author: mmcky

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
 ```