Installation

Installation#

This section describes two type of installation:

We recommend setting up a virtual environment before installing DISDRODB.

Virtual Environment Creation#

Although optional, using a virtual environment when installing DISDRODB is recommended.

Virtual environments isolate dependencies, simplify package management, improve maintainability, enhance security, and streamline your development workflow.

Below are two options for creating a virtual environment, using venv or conda (recommended).

With conda:

  • Install mamba, miniconda or anaconda if you haven’t already installed.

  • Create a new conda environment with one of the latest python version (e.g., python 3.13):

conda create --name disdrodb python=3.13

  • Activate the environment:

conda activate disdrodb

With venv:

  • On Windows, create and activate a virtual environment:

python -m venv disdrodb-pyXXX
cd disdrodb-pyXXX/Scripts
activate

  • On macOS/Linux, create and activate a virtual environment:

python3 -m venv disdrodb-pyXXX
source disdrodb-pyXXX/bin/activate

Installation for standard users#

The latest disdrodb stable version is available on the Python Packaging Index (PyPI) and on the conda-forge channel.

Therefore you can either install the package with pip or conda (recommended). Please install the package in the virtual environment you created before !

With conda:

conda install -c conda-forge disdrodb

Note

In alternative to conda, if you are looking for a lightweight package manager you could use micromamba.

With pip:

pip install disdrodb

Installation for contributors#

The latest disdrodb version is available on the GitHub repository disdrodb. You can install the package in editable mode, so that you can modify the code and see the changes immediately. Here below we provide the steps to install the package in editable mode.

Clone the repository from GitHub#

According to the contributors guidelines, you should first create a fork into your personal GitHub account.

Then create a local copy of the repository you forked with:

git clone https://github.com/<your-account>/disdrodb.git
cd disdrodb

Create the development environment#

We recommend to create a dedicated conda environment for development purposes. You can create a conda environment (i.e. with python 3.13) with:

conda create --name disdrodb-dev python=3.13
conda activate disdrodb-dev

Install the package dependencies#

conda install --only-deps disdrodb

Install the package in editable mode#

Install the disdrodb package in editable mode by executing the following command in the disdrodb repository’s root:

pip install -e ".[dev]"

Install code quality checks#

Install the pre-commit hook by executing the following command in the disdrodb repository’s root:

pre-commit install

Pre-commit hooks are automated scripts that run during each commit to detect basic code quality issues. If a hook identifies an issue (signified by the pre-commit script exiting with a non-zero status), it halts the commit process and displays the error messages.

Note

The versions of the software used in the pre-commit hooks is specified in the .pre-commit-config.yaml file. This file serves as a configuration guide, ensuring that the hooks are executed with the correct versions of each tool, thereby maintaining consistency and reliability in the code quality checks.

Further details about pre-commit hooks can be found in the Contributors Guidelines, specifically in the provided in the Code quality control section.

Optional dependencies#

Specific functionality in disdrodb may require additional optional dependencies. To unlock the full functionalities offered by disdrodb, it is recommended to install also the packages detailed here below.

IDEs#

For an improved development experience, consider installing the intuitive Jupyter and Spyder Python Integrated Development Environments (IDEs):

conda install -c conda-forge jupyter spyder

Speed Up Xarray Computations#

To speed up arrays computations with xarray, install flox, numbagg, bottleneck and opt-einsum:

conda install -c conda-forge flox numbagg bottleneck opt-einsum

Summary Tables#

If you want to generate automatic summary tables of rain events or DSD parameters within the disdrodb software, Tectonic must be installed. Tectonic is a modern typesetting system that can be used to compile LaTeX documents and create PDF files.

You can install Tectonic using conda:

conda install -c conda-forge tectonic

T-Matrix Radar Simulations#

To simulate radar polarimetric variables in the DISDRODB L2E and L2M products, you must install the pyTMatrix package. The original pyTMatrix can be difficult to install on recent Python versions.

The instructions below describe how to install the LTE-maintained fork of pyTMatrix, which is compatible with modern Python interpreters. Please note that it’s currently not possible to install pyTmatrix on Windows systems.

  1. Install the GNU Fortran Compiler (gfortran) and the Meson Build system.

conda install -c conda-forge gfortran meson

  1. Fork and clone the LTE-maintained pyTMatrix repository:

git clone https://github.com/<your-account>/pytmatrix-lte.git

  1. Enter the newly cloned pytmatrix-lte directory and install the package in editable mode:

cd pytmatrix-lte
pip install -e .

  1. To confirm that everything was installed correctly, run the pytmatrix built-in test suite. Launch Python and execute:

from pytmatrix.test import test_tmatrix

test_tmatrix.run_tests()

Warning

Installing pyTMatrix directly via pip install git+https://github.com/ltelab/pytmatrix-lte.git does not work at this time. We welcome contributions to enable this type of installation !

Run DISDRODB on Jupyter Notebooks#

If you want to run disdrodb on a Jupyter Notebook, you have to take care to set up the IPython kernel environment where disdrodb is installed.

For example, if your conda/virtual environment is named disdrodb-dev, run:

python -m ipykernel install --user --name=disdrodb-dev

When you will use the Jupyter Notebook, by clicking on Kernel and then Change Kernel, you will be able to select the disdrodb-dev kernel.