Adding correct documentation.

src/documentation/analysis.rst

@@ -1,14 +1,14 @@
 .. _analysis:
 
 ************************************
-Main model estimations / simulations
+Main model estimations
 ************************************
 
-Documentation of the code in *src.analysis*. This is the core of the project.
+Here we estimate our regressions with the different specifications we have specified in *src/model_specs*.
 
 
-Schelling example
+Regression analysis
 =================
 
-.. automodule:: src.analysis.task_schelling
+.. automodule:: src.analysis.task_regression_analysis
     :members:

src/documentation/index.rst

@@ -13,6 +13,8 @@ Welcome to the covid-19 Mobility project's documentation!
     introduction
     original_data
     data_management
+    model_code
+    model_specs
     analysis
     final
     paper

src/documentation/introduction.rst

@@ -5,33 +5,22 @@
 Introduction
 ************
 
-You can find the documentation on the rationale, pytask, and more background at https://econ-project-templates.readthedocs.io/en/stable/.
-
-The Python version of the template uses a modified version of Stachurski's and Sargent's code accompanying their Online Course :cite:`StachurskiSargent13` for Schelling's (1969, :cite:`Schelling69`) segregation model as the running example.
+Welcome to our project! We (Bahar, Timo and Anto) have built this project to analyze mobility on different levels:
 
+1. We compare German mobility with other european countries' mobility.
+2. We compare German mobility on state level and on pre-specified divisions of Germany such as "City-States vs Non-City-States" and "Former BRD vs DDR".
+3. We analyze whether there is "Lockdown fatigue" in Germany by setting up several regressions with different specifications.
 
 .. _getting_started:
 
 Getting started
 ===============
 
-**This assumes you have completed the steps in the `Getting Started section of the documentation <https://econ-project-templates.readthedocs.io/en/stable/getting_started.html>`_ and **everything worked.**
+**To be able to run our project, please follow the steps in the README file of our Github repository <https://github.com/baharcos/covid_19_mobility>`**
 
-The logic of the project template works by step of the analysis:
+To give you a rough idea of how our project is built, here is a short overview:
 
 1. Data management
-2. The actual estimations / simulations / ?
-3. Visualisation and results formatting (e.g. exporting of LaTeX tables)
-4. Research paper and presentations.
-
-It can be useful to have code and model parameters available to more than one of these steps, in that case see sections :ref:`model_specifications`, :ref:`model_code`, and :ref:`library`.
-
-First of all, think about whether this structure fits your needs -- if it does not, you need to adjust (delete/add/rename) directories and files in the following locations:
-
-    * Directories in **src/**;
-    * The documentation source files in **src/documentation/** (Note: These should follow the directories in **src** exactly);
-    * The list of included documentation source files in **src/documentation/index.rst**
-
-Later adjustments should be painlessly possible, so things won't be set in stone.
-
-Once you have done that, move your source data to **src/original_data/** and start filling up the actual steps of the project workflow (data management, analysis, final steps, paper). All you should need to worry about is to specify the tasks for `pytask`.
+2. Visualization and results formatting (e.g. exporting of LaTeX tables)
+3. Estimations
+4. Presentation.

src/documentation/model_code.rst

@@ -4,13 +4,13 @@
 Model code
 **********
 
-The directory *src.model_code* contains source files that might differ by model and which are potentially used at various steps of the analysis.
+We run several regressions to find out which model represents the data best. The file in this directory produces the different specifications.
+They can be found in the directory *src/model_specs*.
 
-For example, you may have a class that is used both in the :ref:`analysis` and the :ref:`final` steps. Additionally, maybe you have different utility functions in the baseline version and for your robustness check. You can just inherit from the baseline class and override the utility function then.
 
-The ``Agent`` class of the Schelling example
-============================================
+Regression specifications
+=========================
 
-.. automodule:: src.model_code.agent
+.. automodule:: src.model_code.task_regression_specifications
     :members:
 

src/documentation/model_specs.rst

@@ -4,7 +4,9 @@
 Model specifications
 ********************
 
-The directory *src.model_specs* contains `JSON <http://www.json.org/>`_ files with model specifications. The choice of JSON is motivated by the attempt to be language-agnostic: JSON is quite expressive and there are parsers for nearly all languages. [#]_
+We create the following three pickle files containing specification details:
 
+1. regression_models.pkl:
+2. regression_variable_names.pkl:
+3. time_lockdowns:
 
-.. [#] Stata is the only execption I know of. You find a  converter in the pytask file of the Stata branch. Note that there is `insheetjson <http://ideas.repec.org/c/boc/bocode/s457407.html>`_, but that will read a JSON file into the data set rather than into macros, which is what we need here.

src/documentation/original_data.rst

@@ -4,9 +4,8 @@
 Original data
 *************
 
-In this folder, there are four different datasets:
+To be able to conduct our analysis, we need the following three different datasets:
 
-1. apple_data: This dataset contains mobility information generated by Apple users
-2. google_data: This dataset contains mobility information generated by Google users
-3. owid_data: This dataset contains information on COVID-19 numbers (such as infection numbers on country level)
-4. stringency_index_data: This dataset contains the stringency index for different countries
+1. google_data: This dataset contains mobility information generated by Google users
+2. owid_data: This dataset contains information on COVID-19 numbers (such as infection numbers on country level)
+3. stringency_index_data: This dataset contains the stringency index for different countries