pyam: a Python toolkit for Integrated Assessment Modeling
Overview and scope
pyam package provides a range of diagnostic tools and functions
for analyzing and working with IAMC-format timeseries data.
- Summary of models, scenarios, variables, and regions included in a snapshot.
- Display of timeseries data as pandas.DataFrame with IAMC-specific filtering options.
- Simple visualization and plotting functions.
- Diagnostic checks for non-reported variables or timeseries data to identify outliers and potential reporting issues.
- Categorization of scenarios according to timeseries data or meta-identifiers for further analysis.
The package can be used with timeseries data that follows the data template convention of the Integrated Assessment Modeling Consortium (IAMC). An illustrative example is shown below; see data.ene.iiasa.ac.at/database for more information.
|MESSAGE V.4||AMPERE3-Base||World||Primary Energy||EJ/y||454.5||479.6||...|
A comprehensive tutorial for the basic functions is included in tutorial/pyam_first_steps using a partial snapshot of the IPCC AR5 scenario database.
The documentation pages can be built locally. See the instruction in doc/README.
Copyright 2017-2018 IIASA Energy Program
For basic instructions see our website.
To install from source after cloning this repository, simply run
pip install -e .
To setup a development environment,
# pyam can be replaced with any other name # you don't have to specify your python version if you don't want conda create --name pyam pip python=X.Y.Z conda activate pyam # may be source activate pyam or just activate pyam pip install -e .[tests,docs,deploy] # install other required packages (e.g. on a Unix like system) conda install -c conda-forge $(cat requirements.txt) # by hand also works e.g. conda install -c conda-forge cartopy geopandas # if you want to write notebooks pip install notebook nbconvert jupyter_contrib_nbextensions
To check everything has installed correctly,
All the tests should pass.