Merge pull request #10 from QuantEcon/review-matplotlib

MAINT: remove %matplotlib inline and contents directive

Author: mmcky

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/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/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/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/estspec.md

@@ -23,10 +23,6 @@ kernelspec:
 ```{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/finite_markov.md

@@ -20,10 +20,6 @@ kernelspec:
 
 # {index}`Finite Markov Chains <single: Finite Markov Chains>`
 
-```{contents} Contents
-:depth: 2
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython

lectures/five_preferences.md

@@ -73,7 +73,6 @@ from numba import njit
 :tags: [hide-input]
 
 # Plotting parameters
-%matplotlib inline
 %config InlineBackend.figure_format='retina'
 
 plt.rc('text', usetex=True)

lectures/linear_algebra.md

@@ -23,10 +23,6 @@ kernelspec:
 ```{index} single: Linear Algebra
 ```
 
-```{contents} Contents
-:depth: 2
-```
-
 ## Overview
 
 Linear algebra is one of the most useful branches of applied mathematics for economists to invest in.

lectures/lu_tricks.md

@@ -20,10 +20,6 @@ kernelspec:
 
 # Classical Control with Linear Algebra
 
-```{contents} Contents
-:depth: 2
-```
-
 ## Overview
 
 In an earlier lecture [Linear Quadratic Dynamic Programming Problems](https://python-intro.quantecon.org/lqcontrol.html), we have studied how to solve a special
@@ -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
 ```
 
 ### References

lectures/newton_method.md

@@ -24,10 +24,6 @@ kernelspec:
 
 # Using Newton's Method to Solve Economic Models
 
-```{contents} Contents
-:depth: 2
-```
-
 ```{seealso}
 **GPU:** A version of this lecture which makes use of [jax](https://jax.readthedocs.io) to run the code
 on a `GPU` is [available here](https://jax.quantecon.org/newtons_method.html)