Installation¶

Installation is done via pip or conda. See below for detailed instructions.

In all cases, it is highly recommended to install rydiqule in a virtual environment.

Installation via conda is recommended for rydiqule. It handles dependency installation as well as a virtual environment to ensure packages do not conflict with other usages on the same system. Finally, the numpy provided by anaconda has been compiled against optimized BLAS/LAPACK implementations, which results in much better performance in rydiqule itself.

Note

RydIQule currently requires python >3.8. For a new installation, it is recommended to use the newest supported python. Currently supported versions are pythons

Regular Installation¶

conda

Assuming you have not already created a separate environment for RydIQule (recommended), run the following to create a new environment:

(base) ~/> conda create -n rydiqule python=3.11
(base) ~/> conda activate rydiqule

Now install via rydiqule’s anaconda channel. This channel provides rydiqule as well as its dependencies that are not available in the default anaconda channel. If one of these dependencies is outdated, please raise an issue with the vendoring repository.

(rydiqule) ~/> conda install -c rydiqule rydiqule

pip

To install normally, run:

pip install rydiqule

This command will use pip to install all necessary dependencies.

The uv package and project manager allows for unified project management in a way that is reproducible and easy to share. You create a new simulation project that relies on rydiqule by running the following commands.

uv init new-project
cd new-project
uv venv --python 3.12  # this line optionally sets python version to use for the venv

These commands create a project template in the sub-directory new-project. This directory is readily version controlled and contains all information needed to reproduce your environment.

You finish configuring the project by adding required dependencies, starting with rydiqule.

uv add rydiqule

You can now create a script in the project and run it directly:

uv run example.py

You can also add jupyter kernel support and run jupyter notebooks from VS Code. This support is added as a development dependency.

uv add --dev ipykernel

Once added, VS Code can launch a jupyter notebook using the project’s virtual environment located in the /new-project/.venv/ sub-directory.

You can also run jupyter lab directly from the virtual environment with the following command

uv run --with jupyter jupyter lab

Editable Installation¶

If you would like to install rydiqule in editable mode to locally modify its source, use the following commands.

conda

Follow the above to install rydiqule and its dependencies, then run the following to uninstall rydiqule as provided by conda and install the editable local repository.

(rydiqule) ~/> conda remove rydiqule --force
# following must be run from root of local repository
(rydiqule) ~/> pip install -e .

pip

Run the following from the root directory of the cloned repository:

pip install -e .

Using uv on an existing python package automatically installs it in editable mode. Run the following from the root of the local repository.

uv venv --python 3.12  # this line optionally sets a python version for the venv
uv sync

Note that uv sync automatically installs the dev dependency group which includes ipykernel and pytest.

You can now use VS Code to run jupyter notebooks with the virtual environment at rydiqule/.venv/. To use jupyter lab, run the following command

uv run --with jupyter jupyter lab

Note that editable installations should have git available if you want dynamic versioning (via setuptools-scm), either by a system-wide installation or via conda in the virtual environment (conda install git).

Note

While rydiqule is a pure python package (ie it is platform independent), its core dependency ARC is not. If a pre-built package of ARC is not available for your platform in our anaconda channel, you will need to install ARC via pip to build it locally before installing rydiqule. To see what architectures are supported, please see the vendoring repository.

Confirm installation¶

Proper installation can be confirmed by executing the following commands in a python terminal.

>>> import rydiqule as rq
>>> rq.about()

        Rydiqule
    ================

Rydiqule Version:     1.1.0
Installation Path:    ~\Miniconda3\envs\rydiqule\lib\site-packages\rydiqule

      Dependencies
    ================

NumPy Version:        1.24.3
SciPy Version:        1.10.1
Matplotlib Version:   3.7.1
ARC Version:          3.3.0
Python Version:       3.9.16
Python Install Path:  ~\Miniconda3\envs\rydiqule
Platform Info:        Windows (AMD64)
CPU Count:            12
Total System Memory:  128 GB

Updating an existing installation¶

Upgrading an existing installation is simple. Simply run the appropriate upgrade command for the installation method used.

Regular Installation Upgrade¶

conda

conda upgrade rydiqule

pip

# standard upgrade
pip install rydiqule
# greedy upgrade: ie update dependencies too
pip install -U rydiqule

uv lock --upgrade-package rydiqule
# greedy upgrade
uv lock --upgrade

Editable Installation Upgrade¶

If using an editable install, simply replacing the files in the same directory is sufficient. Though it is recommended to also run the appropriate pip update command as well to capture updated dependencies.

conda

pip install -U -e .

pip

pip install -U -e .

uv sync

Note that any uv run command will automatically sync and thereby capture updated dependencies.

Dependencies¶

This package requires installation of the excellent ARC package, which is used to get Rydberg atomic properties. It also requires other standard computation dependencies, such as numpy, scipy, matplotlib, etc. These will be automatically installed if not already present.

Note

Rydiqule’s performance does depend on these dependencies. In particular, numpy can be compiled with a variety of backends that implement BLAS and LAPACK routines that can have different performance for different computer architectures. When using Windows, it is recommended to install numpy from the conda default channel, which is built against the IntelMKL and has generally shown the best performance for Intel-based PCs.

Optional timesolver backend dependencies include the numba and CyRK packages. Both are available via pip, conda, or our anaconda channel.

conda

For conda installations, these dependencies must be installed manually

conda install -c rydiqule CyRK

pip

Backends can be installed automatically via the optional extras specification for the pip command.

pip install rydiqule[backends]

Backends can be installed automatically via the optional extras specification for the uv sync command.

uv sync --extra backends

Note that these dependencies will be uninstalled if uv sync is called without the extras flag.