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pyhector is a Python interface for the simple global climate carbon-cycle model Hector.

pyhector makes the simple climate model Hector easily installable and usable from Python and can for example be used in the analysis of mitigation scenarios, in integrated assessment models, complex climate model emulation, and uncertainty analyses.

Hector is written in C++ and developed at the Pacific Northwest National Laboratory. The model description is published in

Hartin, C. A., Patel, P., Schwarber, A., Link, R. P., and Bond-Lamberty, B. P.: A simple object-oriented and open-source model for scientific and policy analyses of the global climate system – Hector v1.0, Geosci. Model Dev., 8, 939-955, doi:10.5194/gmd-8-939-2015, 2015.

See the Hector repository and wiki for further information.

The Python interface pyhector is developed by Sven Willner and Robert Gieseke.

Pyhector uses pybind11 to wrap Hector's API. The version of Hector used can be read using Pyhector's __hector_version__ field.



Hector requires Boost, so to install and use pyhector you need to have the filesystem and system modules of Boost version 1.52 or later installed (see also the Hector build instructions).

On Ubuntu/Debian these can be installed by invoking

sudo apt-get install libboost-filesystem-dev libboost-system-dev

On macOS Boost is available through the Homebrew package manager, it might be advisable to use a Homebrew installed Python for installing pyhector:

brew install boost

Windows is (as Hector) in principle supported but not yet tested for pyhector. Pull request with installation notes for Windows are welcome.

Install using pip

You can simply install pyhector from PyPI by invoking

pip install pyhector


This repository also contains a Jupyter Notebook you can run live and experiment with, courtesy of the Binder project. The notebook can be viewed as a static version using nbviewer.

Basic example

import pyhector

output =

Advanced example

import pyhector
from pyhector import rcp26, rcp45, rcp60, rcp85

import matplotlib.pyplot as plt

for rcp in [rcp26, rcp45, rcp60, rcp85]:
    output =, {"core": {"endDate": 2100}})
    temp = output["temperature.Tgav"]
    # Adjust to 1850 - 1900 reference period
    temp = temp.loc[1850:] - temp.loc[1850:1900].mean()
plt.title("Global mean temperature")
plt.ylabel("°C over pre-industrial (1850-1900 mean)")

Temperature Plot of RCP scenarios


For local development you can clone the repository, update the dependencies and install in a virtual environment with pip.

git clone --recursive
cd pyhector
python3 -m venv venv
./venv/bin/pip install --editable .

To update pyhector and all submodules you can run

git pull --recurse-submodules
git submodule update --init --recursive
./venv/bin/pip install --editable .

Tests can be run locally with

python test