Abstract | Reproduce Plots

Function | Development | Automated testing through buildLibrary and backwards compatibility | REMIND Variable Naming

Overview | Basic configuration | Example .Rprofile | Multiple models | Launching the app | Selection sets | Variable configuration (YAML)

Overview | 1. r if (!knitr::is_latex_output()) {"📖"} Read function read_io | 2. r if (!knitr::is_latex_output()) {"📁"} Data class LPJmLData | 3. r if (!knitr::is_latex_output()) {"📈"} Basic statistics of LPJmLData objects | 4. r if (!knitr::is_latex_output()) {"✏"} Modify LPJmLData objects | 5. r if (!knitr::is_latex_output()) {"📦"} Export LPJmLData objects | Miscellaneous | Usage | 1. Example Global Trend in net primary productivity (NPP) over the years | 2. Example Runoff in northern hemisphere during summertime | 3. Example Gross primary productivity (GPP) per latitude | 4. Example CFT fractions for area around Potsdam | Notes & tips

Starting Point | Overview | Requirements and Preparation | Files Needed | Software Setup | Install R and configure your environment | Install mrdownscale and development tools | Getting Started with the Integration | Integration Steps | Step 1: Create Reference Mapping | Step 2: Create Read Function | Set up the data folder structure | Create the read function | Test the read function | Step 3: Add to calcLandInput | Step 4: Add to calcResolutionMapping | Step 5: Register in toolLandCategoriesMapping | Running the Harmonization | Running the Full Pipeline - Harmonization and Downscaling | Checklist

magclass vs other data-classes | dataclass: matrix | dataclass: array | dataclass: dataframe | dataclass: magclass | Why use magclass for your work?

Testing | (soon to be) deprecated functions | New feature: extended sub-dimension support | New magclass subsetting variant | Generalized handling of spatial raster data | One-stop-shop-function getItems | Internal code modifications

Generate a magpie object | Accessing magpie objects | General properties | Subsets | Data transformations / calculations

Load required packages | Data Wrangling | Load Data | Trim Data | Use Pretty Names | Piping | Filtering | Selecting | Grouping | Mutating | Summarising | Sum Things Up | Add New Variables | Net Present Value of a Time Series | Calculate Sample Quantiles | Interpolate Missing Periods | Plotting with mip | Plotting 'correct' Bars

The with_regions argument | with_regions = a data-frame with a country-to-region mapping | with_regions = a string with a madrat regionmapping

The replace_NAs argument | replace_NAs = NULL or NA | replace_NAs = 0 | replace_NAs = "no_conversion" | replace_NAs = "linear" | replace_NAs = "regional_average" | replace_NAs = c("linear", "...") | replace_NAs = "with_USA"

Basics | A typical workflow | Making a madrat preprocessing ready for puc-files | Potential problems

Overview | Installation | Usage | REMIND output script | Scenario Validation | Validation Report | Scenario Data | Reference Data | Configuration File | Columns | Using multiple references | Units | Available Configs | Use Cases | Use Case 1: relative comparison to reference data | Use Case 2: difference to reference data | Use Case 3: relative comparison to other model/scenario/period | Use Case 4: difference to other model/scenario/period | Use Case 5: direct comparison to absolute thresholds | Use Case 6: direct comparison to yearly growthrate

The source argument | Package internal 'sources' | User-defined 'source' objects | The use_USA_cf_for_all argument

Overview | Usage | Via output script in REMIND | Via direct function call | Interactive use of the Rmd-files | Using outputFormat = "Rmd" | Using a cloned remind2 repository | Profiles | Existing profiles | Changing or Adding Profiles | Viewing variables available in mif Files | Via package function in remind2 | How to add a new plot to remind2 | Preprocessing | Further Info

Basics | Fingerprint | Customize fingerprinting | Control flags | Examples | forcecache

r if (!knitr::is_latex_output()) {"⚙"} Setup | Overview | 1. r if (!knitr::is_latex_output()) {"📋"} Define a table of modified configuration parameters\ | 2. r if (!knitr::is_latex_output()) {"✍"} Create corresponding Configuration files\ | 3. r if (!knitr::is_latex_output()) {"🔍"} Check validity of Configurations\ | 4. r if (!knitr::is_latex_output()) {"▶"} Run or r if (!knitr::is_latex_output()) {"🚀"} submit LPJmL\ | miscellaneous \ | Usage | Single cell simulations\ | Example Potential natural vegetation and land-use run | Example Old vs. new phenology and old land-use vs. input toolbox | Global simulations on the PIK cluster | Example Compare old vs new land use (lpjml input toolbox) | Notes & tips

Overview | 1. r if (!knitr::is_latex_output()) {"✏"} Calculate boundaries\ | 2. r if (!knitr::is_latex_output()) {"📊"} Plot boundary status(es)\ | 3. r if (!knitr::is_latex_output()) {"📋"} Create global validation table\ | Miscellaneous \ | Usage | Global status timeseries\ | Example Global status calculation with moving average | Example Compare two calculation approaches for the same boundary | Gridded status\ | Example Gridded status calculation and plotting | Regional status\ | Example Regional status calculation and plotting | Plotting external data for PB control variables\ | Example 1 Plotting external gridded data | Example 2 Plotting external time series | Notes & tips

Key scenarios | GDP per capita and GDP | SSPs | SDPs | Population | Labour | Urban population share | Available Scenarios | User Defined Scenarios

Using the benchmark function | Working with the benchmark results | The PDF report | The benchmark result object

Learning objectives | Introduction | Call the (untouched) function | Get the code | Code chunk of a single plot | Area plot | Line plot | Manually executing parts of the function | Add a new plot | Rebuild the package | Generate a pdf containing the new plot | Commit your changes to the repository

Description | Usage | Data generation structure | Scenarios description | References

Overview | Getting Started | Add Rmd files to your reporting library | Run compareScenarios | Interactive use of the Rmd-files | Structure of cs_main.Rmd | YAML-header | Loading | Preprocessing | Global Variables | Custom Code | Section-Rmd-files | userSectionPath | Special Sections | 0 Info | 99 Further Info | Content Functions | Plot Functions | Table Functions

Installation lpjmlkit | Installation biospheremetrics | Preparations | Demo case | BioCol | EcoRisk | Metrics definitions and file names | Example use

Introduction | How it works: | How to use/contribute:

Purpose and Functionality | Running goxygen on plain GAMS code | Goxygen syntax | Additional attributes | extrapage | Further features | Running goxygen on modularized GAMS code | References

Overview | Treatment of data mismatches | Zero Irrigation Water Requirements

Overview | Related libraries | Input data | Motivation | Hydrological input data | Human water use data | Land-use input data | Irrigation Water Potentials (IWP) | Alternative allocation algortihms | Economic yield value gains | Work-in-progress | Cropland extent | Irrigation Area Potential (IAP) | Generation of potential irrigation area (PIA) curves

Overview | Scenario Setting arguments | River Discharge Allocation Arguments

Installation | How to use it | How to run single components | Data processing in mrorganic | Land type data | Soil Organic Carbon data | Aboveground and Belowground Biomass data | Acknowledgement

Local Use and mif-Files | Interactive Use of the Rmd-files | Workflow for Adding a new Plot to compareScenariosLimes | cs2_main.Rmd | YAML-header | Loading | Preprocessing | Global Variables | Section-Rmd-files | userSectionPath | Special Sections | 0 Info | 99 Further Info | Plot Functions

5 common use cases:

Setup | madrat framework components | downloadSource | readSource | read | correct | convert | calcOutput | retrieveData | Coding etiquette | Use own functions with madrat | Create madrat-based package

Example | Further Reading | Group JSON-LD | DataID JSON-LD

Introduction | Installation | Setup | Usage | Debugging

The GDPuc.warn option | The replace_NAs argument

Auto expansion v1 vs v2 | Set matching | Idential set elements, different set names | Different set element, identical set names

The verbose argument | The return_cfs argument | Conversion method

Introduction | The method | Main steps | 1. Perform MAgPIE runs | 1.1 Start emulator runs | 1.2 Calculate bioenergy supply curves | 1.3 Correct emulators if necessary (replace flat fits) | 2. Add to REMIND input data | 2.1 Via mrremind | 2.2 Adding manually | Replace NAs with artificial fits | Use the file in REMIND as base

Overview | Data | River structure | Basin attribution

Setup | Data for all India | Data for individual states | Data for India Zonal Councils | Output to file(s)

Purpose and Functionality | Input data | Examples | Alternative A: Calculate emulator for one single case consisting of all scenarios present in the data | Alternative B: Calculate emulator for multiple cases. Distinguish cases by scenarios present in the data. | How to define your own function to be fitted | Advanced | Replace flat fits | Compare supplycurves

Definition

Purpose and Functionality | Components | Input data | Configuration | Pre-checks | Checks | Validation (in PDF file only) | A simple example | Customizing the project settings | Validate data against historical and other reference data | Adding own checks

Objective | The magpiesets table | Example: | Calling a set | Using set versions | Calling a set of a specific version | Expanding the set space | Examples | More information