Merge pull request #46 from OpenSourceEconomics/formatting_corrections

Fix formatting to properly render equations

README.rst

@@ -62,7 +62,7 @@ Textbooks
   :target: https://mitpress.mit.edu/books/numerical-methods-economics
 
 
-We use the book `Applied computational economics and finance <https://mitpress.mit.edu/books/applied-computational-economics-and-finance>`_ by `Mario Miranda <https://aede.osu.edu/our-people/mario-javier-miranda>`_ and `Paul Fackler <https://pfackler.wordpress.ncsu.edu>`_ throughout the course. A special thanks to `Randall Romero Aguilar <https://github.com/randall-romero>`_ who also build a course around this book and maintains a Python implementation of the `CompEcon <https://github.com/randall-romero/CompEcon>`_ toolbox. Many of our code examples are building on his implementation there. In addition, we will also consult `Numerical methods in economics <https://mitpress.mit.edu/books/numerical-methods-economics>`_ by `Ken Judd <https://kenjudd.org/>`_ for some of the more advanced material.
+We use the book `Applied computational economics and finance <https://mitpress.mit.edu/books/applied-computational-economics-and-finance>`_ by `Mario Miranda <https://aede.osu.edu/our-people/mario-javier-miranda>`_ and `Paul Fackler <https://pfackler.wordpress.ncsu.edu>`_ throughout the course. A special thanks to `Randall Romero Aguilar <https://github.com/randall-romero>`_ who has also built a course around this book and maintains a Python implementation of the `CompEcon <https://github.com/randall-romero/CompEcon>`_ toolbox. Many of our code examples are building on his implementation there. In addition, we will also consult `Numerical methods in economics <https://mitpress.mit.edu/books/numerical-methods-economics>`_ by `Ken Judd <https://kenjudd.org/>`_ for some of the more advanced material.
 
 Reviews
 -------

index.rst

@@ -5,7 +5,7 @@
 
 .. include:: README.rst
    :start-line: 0
-   :end-line: 43
+   :end-line: 42
 
 .. toctree::
    :maxdepth: 1
@@ -16,5 +16,5 @@
    references/references
 
 .. include:: README.rst
-   :start-line: 45
+   :start-line: 43
    :end-line: 96

iteration/lecture_plan_2020.rst

@@ -103,9 +103,4 @@ We then reconvene at the beginning of the new year to discuss some selected issu
 Projects
 """"""""
 
-**Form of submission:** Your grade for this class will be based on a scientific computing project due at the end of the semester. You should present your project in the form of a Jupyter Notebook. The project needs to be coded in Python. You can work on your project using the `Nuvolos.cloud <https://nuvolos.cloud>`_  and share it with Professor Eisenhauer for grading. Alternatively, you can submit the project in the form of a GitHub repository or pull request on an existing repository (depending on your project). Reproducibility is a cornerstone of sound computational work, so please ensure that we can run your project notebook from beginning to end without any error. Please discuss your project idea with Professor Eisenhauer. We suggest settling on a topic by the end of January 2021 so you have enough time to work on your project.
-
-**Topic:** You are free to select a topic of your choice related to the contents of the class. For instance, you could replicate the core results of a computational publication, run a benchmarking exercise for an algorithm, contribute to one of our group's software packages of your choice, or create a notebook similar to the ones presented in the lectures on a computational topic that interests you. Note that several textbooks explore the implementation of involved computational economic models, porting their implementation to Python can serve as a valuable starting point for your project.
-
-**Note for students taking EPP:**
-Participants of the course "Effective Programming Practices for Economists" by Professor Hans-Martin von Gaudecker are welcome to submit their project for grading in both courses. Note that the project still has to fulfill the topic and submission requirements listed above in addition to any requirements stated by the EPP course. Please reach out in the course Zulip chat for any questions about the project.
+All information regarding course projects is collected in the `OSE course projects documentation <https://ose-course-projects.readthedocs.io/en/latest/index.html>`_.

lectures/approximation/notebook.ipynb

@@ -48,7 +48,7 @@
     "\\phi_1(x) &= x \\\\\n",
     "\\phi_2(x) &= x^2 \\\\\n",
     ". \\\\\n",
-    "\\phi_n(x)&= x^n\n",
+    "\\phi_n(x)&= x^n.\n",
     "\\end{align*}"
    ]
   },
@@ -390,12 +390,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-success\">\n",
-    "    <h3>Questions</h3>  \n",
-    "     <ul>\n",
-    "  <li>Why the warnings?</li>\n",
-    "    </ul> \n",
-    "</div>"
+    "### _Question_\n",
+    "\n",
+    "* Why the warnings?\n",
+    "   "
    ]
   },
   {
@@ -512,13 +510,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-warning\">\n",
-    "    <h3>Exercises</h3>  \n",
-    "     <ul>\n",
-    "      <li> Generalize the function to allow to approximate the function with a polynomial of generic degree.</li>\n",
-    "      <li> How does the quality of the approximation change as we increase the number of interpolation points.</li>         \n",
-    "    </ul> \n",
-    "</div>"
+    "### _Exercises_\n",
+    "\n",
+    "1. Generalize the function to allow to approximate the function with a polynomial of generic degree.\n",
+    "2. How does the quality of the approximation change as we increase the number of interpolation points?"
    ]
   },
   {
@@ -1182,12 +1177,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-success\">\n",
-    "    <h3>Questions</h3>  \n",
-    "     <ul>\n",
-    "  <li>How about other ways to place the interpolation nodes?</li>\n",
-    "    </ul> \n",
-    "</div>"
+    "### _Question_\n",
+    "\n",
+    "* How about other ways to place the interpolation nodes?"
    ]
   },
   {
@@ -1289,15 +1281,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-warning\">\n",
-    "    <h3>Exercises</h3>  \n",
-    "     <ul>\n",
-    "      Let's consider two test functions: problem_reciprocal_exponential, problem_kinked \n",
-    "      <li> Visualize both over the range from -1 to 1. What is the key differences in their properties?</li>\n",
-    "      <li> Set up a function that allows you to flexible interpolate both using either Chebychev polynomials (monomial basis, Chebychev nodes) or linear and cubic splines.</li>         \n",
-    "      <li> Compare the performance for the following degrees: 10, 20, 30.</li>  \n",
-    "    </ul> \n",
-    "</div>"
+    "### _Exercises_\n",
+    "\n",
+    "Let's consider two test functions: problem_reciprocal_exponential, problem_kinked. \n",
+    "\n",
+    "1. Visualize both over the range from -1 to 1. What is the key differences in their properties?\n",
+    "2. Set up a function that allows you to flexibly interpolate using either Chebychev polynomials (monomial basis, Chebychev nodes) or linear and cubic splines.         \n",
+    "3. Compare the performance for the following degrees: 10, 20, 30."
    ]
   },
   {
@@ -1604,7 +1594,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,

lectures/index.rst

@@ -68,8 +68,8 @@ Integration
 
 We examine different strategies for the numerical integration of functions. We discuss rules
 based on Newton-Cotes quadrature formulas, Gaussian quadrature, and Monte Carlo methods in the
-uni-dimensional and multi-dimensional case. We conclude by looking comparing the performance of
-the different approaches under different scenarios.
+uni-dimensional and multi-dimensional case. We conclude by comparing the performance of
+each approach under different scenarios.
 
 
 .. toctree::