remove doc derivates

Author: kp992

lectures/functions.md

@@ -73,7 +73,7 @@ The full list of Python built-ins is [here](https://docs.python.org/library/func
 If the built-in functions don't cover what we need, we either need to import
 functions or create our own.
 
-Examples of importing and using functions were given in the {doc}`previous lecture <python_by_example>`
+Examples of importing and using functions were given in the [previous lecture](python_by_example.md)
 
 Here's another one, which tests whether a given year is a leap year:
 
@@ -263,13 +263,13 @@ User-defined functions are important for improving the clarity of your code by
 
 (Writing the same thing twice is [almost always a bad idea](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself))
 
-We will say more about this {doc}`later <writing_good_code>`.
+We will say more about this [later](writing_good_code.md).
 
 ## Applications
 
 ### Random Draws
 
-Consider again this code from the {doc}`previous lecture <python_by_example>`
+Consider again this code from the [previous lecture](python_by_example.md)
 
 ```{code-cell} python3
 ts_length = 100

lectures/need_for_speed.md

@@ -313,5 +313,5 @@ vectorization listed above.
 It does so through something called **just in time (JIT) compilation**,
 which can generate extremely fast and efficient code.
 
-{doc}`Later <numba>` we'll learn how to use Numba to accelerate Python code.
+[Later](numba.md) we'll learn how to use Numba to accelerate Python code.
 

lectures/numba.md

@@ -22,7 +22,7 @@ In addition to what's in Anaconda, this lecture will need the following librarie
 ```
 
 Please also make sure that you have the latest version of Anaconda, since old
-versions are a {doc}`common source of errors <troubleshooting>`.
+versions are a [common source of errors](troubleshooting.md).
 
 Let's start with some imports:
 
@@ -34,7 +34,7 @@ import matplotlib.pyplot as plt
 
 ## Overview
 
-In an {doc}`earlier lecture <need_for_speed>` we learned about vectorization, which is one method to improve speed and efficiency in numerical work.
+In an [earlier lecture](need_for_speed.md) we learned about vectorization, which is one method to improve speed and efficiency in numerical work.
 
 Vectorization involves sending array processing
 operations in batch to efficient low-level code.
@@ -161,7 +161,7 @@ Numba attempts to generate fast machine code using the infrastructure provided b
 
 It does this by inferring type information on the fly.
 
-(See our {doc}`earlier lecture <need_for_speed>` on scientific computing for a discussion of types.)
+(See our [earlier lecture](need_for_speed.md) on scientific computing for a discussion of types.)
 
 The basic idea is this:
 
@@ -193,7 +193,7 @@ qm_numba = jit(qm)
 
 In practice this would typically be done using an alternative *decorator* syntax.
 
-(We discuss decorators in a {doc}`separate lecture <python_advanced_features>` but you can skip the details at this stage.)
+(We discuss decorators in a [separate lecture](python_advanced_features.md) but you can skip the details at this stage.)
 
 Let's see how this is done.
 
@@ -295,7 +295,7 @@ If a class is successfully compiled, then its methods act as JIT-compiled
 functions.
 
 To give one example, let's consider the class for analyzing the Solow growth model we
-created in {doc}`this lecture <python_oop>`.
+created in [this lecture](python_oop.md).
 
 To compile this class we use the `@jitclass` decorator:
 
@@ -410,7 +410,7 @@ If you prefer, you can safely skip this section.
 
 ### Cython
 
-Like {doc}`Numba <numba>`,  [Cython](http://cython.org/) provides an approach to generating fast compiled code that can be used from Python.
+Like [Numba](numba.md),  [Cython](http://cython.org/) provides an approach to generating fast compiled code that can be used from Python.
 
 As was the case with Numba, a key problem is the fact that Python is dynamically typed.
 

lectures/numpy.md

@@ -956,7 +956,7 @@ a
 What's happened is that we have changed `a` by changing `b`.
 
 The name `b` is bound to `a` and becomes just another reference to the
-array (the Python assignment model is described in more detail {doc}`later in the course <python_advanced_features>`).
+array the Python assignment model is described in more detail [later in the course](python_advanced_features.md).
 
 Hence, it has equal rights to make changes to that array.
 
@@ -1149,7 +1149,7 @@ np.linalg.inv(A)           # Compute the inverse
 
 Much of this functionality is also available in [SciPy](http://www.scipy.org/), a collection of modules that are built on top of NumPy.
 
-We'll cover the SciPy versions in more detail {doc}`soon <scipy>`.
+We'll cover the SciPy versions in more detail [soon](scipy.md).
 
 For a comprehensive list of what's available in NumPy see [this documentation](https://docs.scipy.org/doc/numpy/reference/routines.html).
 
@@ -1159,7 +1159,7 @@ For a comprehensive list of what's available in NumPy see [this documentation](h
 ```{index} single: Vectorization; Operations on Arrays
 ```
 
-We mentioned in an {doc}`previous lecture <need_for_speed>` that NumPy-based vectorization can
+We mentioned in an [previous lecture](need_for_speed.md) that NumPy-based vectorization can
 accelerate scientific applications.
 
 In this section we try some speed comparisons to illustrate this fact.

lectures/oop_intro.md

@@ -287,7 +287,7 @@ If Python is object oriented, why don't we use `x.len()`?
 The answer is related to the fact that Python aims for readability and consistent style.
 
 In Python, it is common for users to build custom objects --- we discuss how to
-do this {doc}`later <python_oop>`.
+do this [later](python_oop.md).
 
 It's quite common for users to add methods to their that measure the length of
 the object, suitably defined.

lectures/pandas_panel.md

@@ -34,7 +34,7 @@ sns.set_theme()
 
 ## Overview
 
-In an {doc}`earlier lecture on pandas <pandas>`, we looked at working with simple data sets.
+In an [earlier lecture on pandas](pandas.md), we looked at working with simple data sets.
 
 Econometricians often need to work with more complex data sets, such as panels.
 

lectures/parallelization.md

@@ -477,7 +477,7 @@ on the number of CPUs on your machine.)
 ```{exercise}
 :label: parallel_ex2
 
-In {doc}`our lecture on SciPy<scipy>`, we discussed pricing a call option in a
+In [our lecture on SciPy](scipy.md), we discussed pricing a call option in a
 setting where the underlying stock price had a simple and well-known
 distribution.
 

lectures/python_by_example.md

@@ -25,7 +25,7 @@ The objective is to introduce you to basic Python syntax and data structures.
 
 Deeper concepts will be covered in later lectures.
 
-You should have read the {doc}`lecture <getting_started>` on getting started with Python before beginning this one.
+You should have read the [lecture](getting_started.md) on getting started with Python before beginning this one.
 
 
 ## The Task: Plotting a White Noise Process
@@ -67,7 +67,7 @@ Let's break this program down and see how it works.
 The first two lines of the program import functionality from external code
 libraries.
 
-The first line imports {doc}`NumPy <numpy>`, a favorite Python package for tasks like
+The first line imports [NumPy](numpy.md), a favorite Python package for tasks like
 
 * working with arrays (vectors and matrices)
 * common mathematical functions like `cos` and `sqrt`
@@ -250,7 +250,7 @@ x
 
 Here `append()` is what's called a **method**, which is a function "attached to" an object---in this case, the list `x`.
 
-We'll learn all about methods {doc}`later on <oop_intro>`, but just to give you some idea, 
+We'll learn all about methods [later on](oop_intro.md), but just to give you some idea, 
 
 * Python objects such as lists, strings, etc. all have methods that are used to manipulate data contained in the object.
 * String objects have [string methods](https://docs.python.org/3/library/stdtypes.html#string-methods), list objects have [list methods](https://docs.python.org/3/tutorial/datastructures.html#more-on-lists), etc.

lectures/python_oop.md

@@ -16,7 +16,7 @@ kernelspec:
 
 ## Overview
 
-In an {doc}`earlier lecture <oop_intro>`, we learned some foundations of object-oriented programming.
+In an [earlier lecture](oop_intro.md), we learned some foundations of object-oriented programming.
 
 The objectives of this lecture are
 
@@ -68,7 +68,7 @@ Let's cover general OOP concepts before we specialize to Python.
 ```{index} single: Object-Oriented Programming; Key Concepts
 ```
 
-As discussed an {doc}`earlier lecture <oop_intro>`, in the OOP paradigm, data and functions are **bundled together** into "objects".
+As discussed an [earlier lecture](oop_intro.md), in the OOP paradigm, data and functions are **bundled together** into "objects".
 
 An example is a Python list, which not only stores data but also knows how to sort itself, etc.
 

lectures/troubleshooting.md

@@ -21,7 +21,7 @@ The basic assumption of the lectures is that code in a lecture should execute wh
 1. it is executed in a Jupyter notebook and
 1. the notebook is running on a machine with the latest version of Anaconda Python.
 
-You have installed Anaconda, haven't you, following the instructions in {doc}`this lecture <getting_started>`?
+You have installed Anaconda, haven't you, following the instructions in [this lecture](getting_started.md)?
 
 Assuming that you have, the most common source of problems for our readers is that their Anaconda distribution is not up to date.
 

lectures/writing_good_code.md

@@ -207,7 +207,7 @@ While the odd global in small scripts is no big deal, we recommend that you teac
 
 For scientific computing, there is another good reason to avoid global variables.
 
-As {doc}`we've seen in previous lectures <numba>`, JIT compilation can generate excellent performance for scripting languages like Python.
+As [we've seen in previous lectures](numba.md), JIT compilation can generate excellent performance for scripting languages like Python.
 
 But the task of the compiler used for JIT compilation becomes harder when global variables are present.