Getting started

Installation

To use the mrio toolbox, install it using pip. It is recommended to use a virtual environment.

(.venv) $ pip install mrio-toolbox

You can also clone the latest version from the GitHub repository:

(.venv) $ git clone https://gitlab.pik-potsdam.de/mrio_toolbox/mrio-toolbox.git

If you install via git, make sure to update the system path to include the local repository. You can do this by adding the following lines to your script:

import sys
sys.path.append('/path/to/mrio-toolbox')

before you import the library to your script as follows:

from mrio_toolbox import MRIO, Part

Contents of the library

Core functions

The MRIO class is the centerpiece of the library. Each MRIO object holds a collection of parts, which themselves represent different aspects of the table, for example the inter-industry matrix, the final demand matrix, the value added etc.

  • The MRIO class provides methods to manipulate all parts, e.g. to load new parts, update groupings, aggregate data etc

  • The Parts class provides methods to manipulate individual parts, e.g. to sum along different axes, select a partition, compute a leontief inverse etc.

Utilities

The library has additional utilities:

  • Extractor functions to read in raw MRIO tables from various providers.

  • Saver functions to save MRIO objects to .nc files

  • Loader functions to load MRIO objects from .npy, .csv, .excel or .nc files

  • A multi-scale mapping algorithm to map PRIMAP emission data into MRIO tables.

For a detailled description of the classes and their methods see the API reference. or the Example Use section.

Read in data tables

The toolbox has extractor functions that support the the following MRIO tables:

  • Eora26

  • Exiobase3

  • Figaro

  • Gloria

  • GTAP11

  • ICIO

  • WIOD

There is an automatic downloader for the Figaro database from Eurostat, see the example notebook

For the other tables, please download the data manually from the respective websites and run the extractor function to safe the data in the netCDF format used by the toolbox.

For example, to extract the Eora26 table of the year 2015, use the following code:

import os
from mrio_toolbox import MRIO, extract_MRIO

source_path = "/path/to/downloaded/eora26/files"
destination_path = "/path/to/save/formatted/eora26/files"

extract_MRIO(
   table="eora26",
   year = "2015",
   source= source_path,
   destination= destination_path
)

file_path = os.path.join(destination_path, f"eora26_year2015.nc")
mrio = MRIO(file = file_path)

Note that if we provide a destination keyword, the extractor function saves the formatted files with the naming convention {table}_year{year}.nc in the specified destination folder.