Merge pull request #148 from dklein-pik/develop

Add link to the documentation of the mif file format.

README.md

@@ -174,6 +174,10 @@ In case that these recommendations can not be followed we would be happy if you
 could discuss that issue with the REMIND development team
 (remind@pik-potsdam.de).
 
+## MODEL OUTPUT
+
+By default the results for a model run are written to an individual results folder within the "output/" folder of the model. The two most important output files are the fulldata.gdx and the REMIND_generic_*scenario-name*.mif. The fulldata.gdx is the technical output of the GAMS optimization and contains all quantities that were used during the optimization in unchanged form. The mif-file is a csv file of certain format and is synthetized from the fulldata.gdx by post-processing scripts. It can be read in any text editor or spreadsheet program and is well suited for a quick look at the results and for further analysis.
+
 ## CONTACT
 remind@pik-potsdam.de
 

tutorials/5_AnalysingModelOutputs.md

@@ -31,7 +31,7 @@ For each scenario, results are written to a folder that is created automatically
 2. Model output files
 =====================
 
-As mentioned in tutorial 2, the two main output files you will typically care about are the *fulldata.gdx* and the *REMIND_generic_NameofYourRun.mif* files in the *output* folder of your run. The *fulldata.gdx* is the actual technical output of the GAMS optimization and contains all the variables, parameters, sets etc. (the differences between these GAMS objects are explained in tutorial 2) of the REMIND model. However, this gdx-file is mainly interesting once you actually work on the GAMS code and want to check specific the variables and their values. If you simply want to look at REMIND results of your run or use it for further data analysis and plotting, you would open the *REMIND_generic_NameofYourRun.mif* which is basically a csv-file in a certain standardized format used in the Integrated Assessment Modeling community. 
+As mentioned in tutorial 2, the two main output files you will typically care about are the *fulldata.gdx* and the *REMIND_generic_NameofYourRun.mif* files in the *output* folder of your run. The *fulldata.gdx* is the actual technical output of the GAMS optimization and contains all the variables, parameters, sets etc. (the differences between these GAMS objects are explained in tutorial 2) of the REMIND model. However, this gdx-file is mainly interesting once you actually work on the GAMS code and want to check specific the variables and their values. If you simply want to look at REMIND results of your run or use it for further data analysis and plotting, you would open the *REMIND_generic_NameofYourRun.mif* which is basically a csv-file in a certain standardized format (called the model intercomparison file format) used in the Integrated Assessment Modeling community. Please refer to the `vignette("mif")` of the package *mip* (model intercomparison plots) to learn more about the mif format.
 
 Looking at the *REMIND_generic_NameofYourRun.mif*, the column **scenario** gives the name of the run (that you specified in the first column of your config file when starting the run). The column **region** provides an three-letter acronym of the region (e.g. EUR -> EU, SSA -> Sub-Saharan Africa). The column **variable** represents the variable you are looking at (To avoid confusion with the above: It does not necessarily represent a variable in the GAMS code of REMIND. The mif-file is a synthetized output generated from *fulldata.gdx* by post-processing Rscripts from the *remind* package). Scrolling through the **variable** column, you will get an impression of the outputs the REMIND model permits you to explore. 
 
@@ -90,6 +90,8 @@ demPE  <- readGDX(gdx,name=c("vm_demPe","v_pedem"),field="l",restore_zeros=FALSE
 ```
 Here **gdx** is the path to the gdx file, while the second argument is the **name** of the GAMS object you want to load. It is possible to extract various GAMS objects like *"sets"*, *"equations"*, *"parameters"*, *"variables"* and *"aliases"* with **readGDX**. With the arguemtn *field="l"*, you can select the levels of endogenous variables. With *field="m"* you can extract the marginal values of these variables.
 
+**To learn how to produce nice graphs from the model output you read in above please refer to [8_Advanced_AnalysingModelOutputs](./8_Advanced_AnalysingModelOutputs.Rmd)**
+
 ---
 
 **In the following, we present several other tools and scripts that were developed to facilitate the output analysis:**