update source files to use latest tomyst(PR#69)

Author: mmcky

conf.py

@@ -40,7 +40,7 @@
 	'IPython.sphinxext.ipython_console_highlighting',
     # Custom Sphinx Extensions
     'sphinxcontrib.jupyter', 
-    'myst_parser',
+    'myst_nb',
 ]
 
 # Retired Extensions but may be useful in Future
@@ -483,3 +483,4 @@
 
 # Set Destination path
 tomyst_static_file_path = ['source/rst/_static']
+tomyst_parser = "myst_nb"

source/rst/404.md

@@ -1,3 +1,14 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  language: python
+  name: python3
+---
+
 # Page Not Found
 
 ```{raw} html

source/rst/about_lectures.md

@@ -1,3 +1,14 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  language: python
+  name: python3
+---
+
 # About Lectures
 
 This is one of a series of online texts on modern quantitative

source/rst/about_py.md

@@ -1,3 +1,14 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  language: python
+  name: python3
+---
+
 ```{raw} html
 <div id="qe-notebook-header" align="right" style="text-align:right;">
         <a href="https://quantecon.org/" title="quantecon.org">
@@ -161,7 +172,7 @@ NumPy provides the basic array data type plus some simple processing operations.
 
 For example, let's build some arrays
 
-```{code-block} python3
+```{code-cell} python3
 import numpy as np                     # Load the library
 
 a = np.linspace(-np.pi, np.pi, 100)    # Create even grid from -π to π
@@ -171,7 +182,7 @@ c = np.sin(a)                          # Apply sin to each element of a
 
 Now let's take the inner product
 
-```{code-block} python3
+```{code-cell} python3
 b @ c
 ```
 
@@ -183,7 +194,7 @@ The [SciPy](http://www.scipy.org) library is built on top of NumPy and provides
 
 For example, let's calculate $\int_{-2}^2 \phi(z) dz$ where $\phi$ is the standard normal density.
 
-```{code-block} python3
+```{code-cell} python3
 from scipy.stats import norm
 from scipy.integrate import quad
 
@@ -251,7 +262,7 @@ single: SymPy
 
 The [SymPy](http://www.sympy.org/) library provides this functionality from within the Python shell.
 
-```{code-block} python3
+```{code-cell} python3
 from sympy import Symbol
 
 x, y = Symbol('x'), Symbol('y')  # Treat 'x' and 'y' as algebraic symbols
@@ -260,32 +271,32 @@ x + x + x + y
 
 We can manipulate expressions
 
-```{code-block} python3
+```{code-cell} python3
 expression = (x + y)**2
 expression.expand()
 ```
 
 solve polynomials
 
-```{code-block} python3
+```{code-cell} python3
 from sympy import solve
 
 solve(x**2 + x + 2)
 ```
 
 and calculate limits, derivatives and integrals
 
-```{code-block} python3
+```{code-cell} python3
 from sympy import limit, sin, diff
 
 limit(1 / x, x, 0)
 ```
 
-```{code-block} python3
+```{code-cell} python3
 limit(sin(x) / x, x, 0)
 ```
 
-```{code-block} python3
+```{code-cell} python3
 diff(sin(x), x)
 ```
 
@@ -313,7 +324,7 @@ Pandas is fast, efficient, flexible and well designed.
 Here's a simple example, using some dummy data generated with Numpy's excellent
 `random` functionality.
 
-```{code-block} python3
+```{code-cell} python3
 import pandas as pd
 np.random.seed(1234)
 
@@ -324,7 +335,7 @@ df = pd.DataFrame(data, columns=('price', 'weight'), index=dates)
 print(df)
 ```
 
-```{code-block} python3
+```{code-cell} python3
 df.mean()
 ```
 
@@ -370,7 +381,7 @@ Its features include, among many other things:
 
 Here's some example code that generates and plots a random graph, with node color determined by shortest path length from a central node.
 
-```{code-block} ipython
+```{code-cell} ipython
 import networkx as nx
 import matplotlib.pyplot as plt
 %matplotlib inline

source/rst/debugging.md

@@ -1,3 +1,14 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  language: python
+  name: python3
+---
+
 ```{raw} html
 <div id="qe-notebook-header" align="right" style="text-align:right;">
         <a href="https://quantecon.org/" title="quantecon.org">
@@ -38,7 +49,7 @@ Here we'll focus on Jupyter and leave you to explore other settings.
 
 We'll need the following imports
 
-```{code-block} ipython
+```{code-cell} ipython
 import numpy as np
 import matplotlib.pyplot as plt
 %matplotlib inline
@@ -54,7 +65,10 @@ single: Debugging
 
 Let's consider a simple (and rather contrived) example
 
-```{code-block} ipython
+```{code-cell} ipython
+---
+tags: [raises-exception]
+---
 def plot_log():
     fig, ax = plt.subplots(2, 1)
     x = np.linspace(1, 2, 10)
@@ -78,7 +92,10 @@ But let's pretend that we don't understand this for the moment.
 
 We might suspect there's something wrong with `ax` but when we try to investigate this object, we get the following exception:
 
-```{code-block} python3
+```{code-cell} python3
+---
+tags: [raises-exception]
+---
 ax
 ```
 
@@ -89,7 +106,10 @@ Let's try doing it a different way.
 
 We run the first cell block again, generating the same error
 
-```{code-block} python3
+```{code-cell} python3
+---
+tags: [raises-exception]
+---
 def plot_log():
     fig, ax = plt.subplots(2, 1)
     x = np.linspace(1, 2, 10)
@@ -161,7 +181,10 @@ The preceding approach is handy but sometimes insufficient.
 
 Consider the following modified version of our function above
 
-```{code-block} python3
+```{code-cell} python3
+---
+tags: [raises-exception]
+---
 def plot_log():
     fig, ax = plt.subplots()
     x = np.logspace(1, 2, 10)