use ifp as a test case'

Author: mmcky

.gitignore

@@ -0,0 +1,2 @@
+_build/
+lectures/_build/

lectures/ifp.md

@@ -23,6 +23,10 @@ kernelspec:
 :depth: 2
 ```
 
+```{code-cell} ipython
+%load _static/lecture_specific/cake_eating_numerical/analytical.py
+```
+
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython
@@ -476,7 +480,8 @@ start to iterate.
 The following function iterates to convergence and returns the approximate
 optimal policy.
 
-```{literalinclude} _static/lecture_specific/coleman_policy_iter/solve_time_iter.py
+```{code-cell} python3
+%load _static/lecture_specific/coleman_policy_iter/solve_time_iter.py
 ```
 
 Let's carry this out using the default parameters of the `IFP` class:
@@ -519,7 +524,8 @@ In this case, our income fluctuation problem is just a cake eating problem.
 We know that, in this case, the value function and optimal consumption policy
 are given by
 
-```{literalinclude} _static/lecture_specific/cake_eating_numerical/analytical.py
+```{code-cell} python3
+%load _static/lecture_specific/cake_eating_numerical/analytical.py
 ```
 
 Let's see if we match up: