Installing Python and packages#

A bit of background#

Here’s a an overview of a few relevant terms:

  • Interpreter: pragmatically defined, an interpreter is a computer program that executes instructions interactively.

    • The python.exe you find in your Python installation is an interpreter. It will run commands one by one.

    • Interpreted programming languages are often contrasted against compiled programming languages.

    • Python is an interpreted language, as is R, for example.

  • Compilation: the act of translating a program from source code (human-readable text form) to machine code.

    • Machine code is the set of low level instructions that a processor understands.

    • A compiled program doesn’t run line by line, but rather builds to entire program first, and then be called to execute. We’re greatly simplifying, but the general idea is: building the entire program at once allows for optimization. This means that compiled languages are generally (much) faster than interpreted languages.

    • C, C++, and Fortran are examples of compiled languages.

    • The most popular Python interpreter, CPython, is a compiled program written in C!

    • Depending on operating system (Linux, MacOS, Windows) and processor architecture(e.g. x86, ARM; 32 bit versus 64 bit), different machine code has to be generated.

And a few notes specific to Python:

  • A Python installation includes the Python interpreter, and the Python standard library: modules to deal with dates, calendars, mathematics, file handling, etc.

  • Package: a collection of files that provide a certain set of functionality. For example: the numpy package for working with large, multi-dimension arrays and matrices.

  • A Python package generally consists of several modules. These are generally the invididual .py files.

  • In terms of files: packages can exist out of single module (.py file), or a set of directories “marked” with a __init__.py file.

Generally, we would like to write the entire program in one language. Due to slowness of interpreted languages, this has not been feasible within Python; especially for technical computing since we typically want to crunch large amounts of numbers. The solution people have come up with is to write the performance critical parts in a compiled language like C or Fortran, and then intermittently call these programs from Python. This solves the performance problem, but at a price: while the package previously consisted of simply a set of Python modules (.py text files), it now includes compiled (binary) files, that are specific to your operating system and processor architecture. Not surprisingly, these are more complicated to distribute.

Dependency conflicts#

Multiple packages often share dependencies, but they might depend on different, incompatible, versions. For example, package B and C might both rely on package A; but B relies on version A.1, and C relies on version A.2.

Typically, you might succeed in installing a version of B and C that use the same version of A, but you’ll find that suddenly package D (which also depends on a version of A) will no longer work. This state of affairs is colloquially called “dependency hell”.

Installing Python packages – without the agonizing pain#

We highly recommend installing packages using pixi or alternatively conda

Pixi and conda are package- and environment-managers that install packages from a remote repository (which is a remote storage location of software packages). Pip (acronym for “Pip install packages”) can also be used for installing Python packages, but was designed mainly to install pure Python packages, without binary dependencies; trying to pip install packages with complex depencies is therefore a recipe for frustration and disaster.

Pixi and conda, do several things. First and foremost, they solve the dependency problem when installing a package. Secondly, they also install binary dependencies. Thirdly, they provides isolated Python installations (termed environments). You might create a new environment if you have unsatisfiable version requirements, like two versions of Python (e.g. 2.7 and 3.9).

Additionally, pixi allows you to store an environment in a specific state in a textfile, allowing you to share envrionments with colleagues. Conda and pip have similar functionality, but this is by far not as robust as pixi implemented this.

Some packages cannot be installed by conda/pixi because they are not available on the conda channels. In that case, with pixi you can install these from PyPI (Python Package Index) as follows:

pixi add <package_name> --pypi

With conda you can fall back on pip to install the package:

pip install <package_name>

Find the articles: Let’s stop dependency hell, Understanding conda and pip and Conda:Myths and Misconceptions for additional information. See also the pixi docs.

Setting up an environment#

At some point you will run into a dependency issue. Sometimes the dependency requirements of two packages are straight out unsatisfiable. In other cases, you’d like to use the latest version, but this would break other packages. The solution used by imod-python is using Pixi. Pixi creates a complete Python installation with all necessary dependencies. Creating a new environment will result in a new Python installation, without sharing of dependencies with other environments. (This is hardly the most efficient use of your hard disk space from a theoretical perspective, but it greatly simplifies matters in the practical sense.)

To create a Pixi environment open a command prompt and cd to the imod folder. In the imod folder use the following command:

pixi run install

This will create a conda environment inside the imod folder. To activate the environment run:

pixi shell

This essentially temporarily updates your PATH variable, which is the set of directories where executable programs are located. closing the command prompt, these directories are removed from PATH again so that the Python installation is properly isolated. Read more at the full pixi docs.

Use the following command to have a pixi environment with ipython, ipykernel, and jupyter for interactive scripting (e.g. plotting data with matplotlib):

pixi run --environment interactive install

Then start the interactive environment:

pixi shell --environment interactive

Installing a development version of iMOD Python#

Since we’re currently in the process of adding a lot of features, the version on PyPI or conda-forge doesn’t always install the carry the latest updates. To get the latest version, activate the environment, clone the reposistory to a repository of choice, and do a “development install”:

activate imod
git clone https://github.com/Deltares/imod-python.git
cd imod-python
pip install -e .

To get the latest developments at a later point in time, execute within the imod-python directory:

git pull

Past versions can also be found on the iMOD Python releases page.

Anaconda, Miniconda, Miniforge#

Ananaconda and Miniconda both provide a Python installation and conda as the package manager. The difference between them is that Anaconda comes with a large number of packages pre-installed in the base environment (which is why the installation is over a gigabyte). Miniconda, on the other hand, comes bare bones. Since we recommend working from environments to install packages into (see below), we do not consider the full Anaconda installer attractive.

Note

Since April 2020, Anaconda has changed their Terms of Service, limiting use of the anaconda repository to commercial users. If you worry you fall in the category of commercial users, we recommend installing Miniforge instead. The change has negligible consequences for imod users, as we’ve long fully relied on the community led conda-forge channel.

You can find the Miniforge homepage and the Miniforge installers here:

You can find installers for Miniconda here: