Installation Guide

Installation Guide#

Welcome to the easyclimate installation guide! 🚀 We’re excited to help you get started with our powerful climate analysis tool. Follow these simple steps to install easyclimate on your system.

The easyclimate package is currently built and tested for specific platforms due to compatibility and dependency constraints. Below are the supported platforms and notes for users on other systems.

  • Windows x86-64/AMD64

  • Linux x86-64/AMD64

These platforms are fully tested, and pre-built wheels (.whl) are available on PyPI for easy installation via following methods:

Using the PyPI package manager:

python -m pip install easyclimate

If you don’t have pip installed, this Python installation guide can guide you through the process.

🛠️ Support is coming soon! Stay tuned for updates—we’re working on it!

You can use PyPI to install the latest unreleased version from GitHub (⚠️ NOT recommended in most situations):

python -m pip install --upgrade git+https://github.com/shenyulu/easyclimate@dev

Note

The commands above should be executed in a terminal. On Windows, use the cmd.exe or the “Anaconda Prompt” app if you’re using Anaconda.

Python Version Requirement#

easyclimate requires Python 3.10 or higher. To check your Python version, run:

python --version

Make sure you’re up to date! 🐍

Dependencies#

easyclimate comes with all the necessary dependencies for a smooth experience. Here’s what gets installed:

Essential packages for core functionality.

numpy >= 1.24.3
xarray >= 2014.10.0
geocat.viz
cartopy >= 0.20
xeofs >= 3.0.0
matplotlib
pandas >= 2.2.0
fast-barnes-py
oceans
intel-fortran-rt
dpcpp-cpp-rt
scipy >=1.8.0
statsmodels
geocat-comp
easyclimate-backend >= 2025.4.0
dask
pymannkendall
flox
xarray_regrid
gsw_xarray
pooch
tqdm
zarr
metpy
tcpypi
netCDF4
h5netcdf
seaborn
EMD-signal
pybarnes

Packages needed for running tests.

numpy >= 1.24.3
xarray >= 2014.10.0
geocat.viz
cartopy >= 0.20
xeofs >= 3.0.0
matplotlib
pandas >= 2.2.0
fast-barnes-py
oceans
intel-fortran-rt
dpcpp-cpp-rt
scipy >=1.8.0
statsmodels
geocat-comp
easyclimate-backend >= 2025.4.0
dask
pymannkendall
flox
xarray_regrid
gsw_xarray
pooch
tqdm
zarr
metpy
tcpypi
netCDF4
h5netcdf
seaborn
EMD-signal
pybarnes

pytest == 8.3.3
pytest-cov == 5.0.0
pytest-mpl == 0.17.0
pre-commit
rich
twine

Tools for building the documentation.

numpy >= 1.24.3
xarray >= 2014.10.0
geocat.viz
cartopy >= 0.20
xeofs >= 3.0.0
matplotlib
pandas >= 2.2.0
fast-barnes-py
oceans
intel-fortran-rt
dpcpp-cpp-rt
scipy >=1.8.0
statsmodels
geocat-comp
easyclimate-backend >= 2025.4.0
dask
pymannkendall
flox
xarray_regrid
gsw_xarray
pooch
tqdm
zarr
metpy
tcpypi
netCDF4
h5netcdf
seaborn
EMD-signal
pybarnes

sphinx == 8.1.3
recommonmark == 0.7.1
sphinx-markdown-tables == 0.0.17
sphinxcontrib-jupyter == 0.5.10
sphinx-inline-tabs == 2023.4.21
sphinx-gallery == 0.19.0
sphinx-autoapi == 3.6.0
sphinx-copybutton == 0.5.2
sphinx-book-theme == 1.1.4
sphinx_design == 0.6.1

Building the Documentation#

Want to build the documentation yourself? 📚 Follow these steps:

  • Install the docs build requirements listed above.
    pip install -r docs/requirements.txt

  • Go to the docs directory.

  • Run the build script:
    .\build_docs_windows.ps1

    Hint

    On Windows, we’ve included optipng.exe for you! 😉 You might NOT need to install optipng for image optimization.

    ./build_docs_linux.sh

    Hint

    On Linux, you might need to install optipng for image optimization.

    sudo apt-get install optipng

Tip

For more control, you need to clean the build directory, build the HTML documentation, and copy example notebooks.

We hope this guide makes installing easyclimate a breeze! If you have any questions or run into issues, feel free to reach out. Happy climate analyzing! 🌍

About easyclimate-backend#

Easyclimate-backend is the core powerhouse behind the easyclimate front-end package, providing a suite of high-performance, low-level functions for climate data analysis. Implemented in languages like Fortran and C, these functions ensure that your climate data processing is both efficient and accurate.

Because of this, you may also need to install a pre-compiled package or compile it yourself on Windows, Linux, or manylinux package.

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