Installation guide

The WelDX package can be installed using any conda or mamba package manager from the Conda-Forge channel. If you have not yet installed a conda package manager, we recommend installing Miniforge. The installer can then be found here, and a detailed documentation for the installation process is provided here. Once this step has been completed, you will gain access to both the conda and the mamba command and will be able to proceed with the installation of the WelDX package.

If you freshly installed Miniforge, open the installed Anaconda Prompt command line interface and run:

conda init
mamba init

to initialize conda and mamba as commands in all your shells.

In order to create a new conda environment called weldx containing the WeldX package, run the console command:

conda create --name weldx --channel conda-forge weldx weldx_widgets

To install the WeldX package into your existing environment instead, use:

conda install weldx weldx_widgets --channel conda-forge

If installed, all conda commands can be replaced by mamba to take advantage of its faster solver.

The package is also available on pypi and can be installed via:

pip install weldx weldx-widgets

As weldx currently depends on the package bottleneck, which contains C/C++ code, you will need a working C/C++ compiler. The conda package does not have this requirement as it only installs pre-compiled binaries. So if you do not know how to install a working compiler, we strongly encourage using the conda package.

Setting up Jupyter Lab

Weldx provides many visualisation methods for planning and analysis. These methods require a frontend such as JupyterLab or Jupyter notebook. We recommend using JupyterLab because it is modern and makes it easy to work with several notebooks. You can install JupyterLab using either conda, mamba, or pip. If you use conda, we recommend that you create a separate environment for your weldx installation and Jupyter. This will keep the environments clean and easier to upgrade.

Create an environment named jlab via conda that installs jupyterlab and the k3d extension:

conda create --name jlab --channel conda-forge jupyterlab k3d

Then we switch to the weldx environment created in the first step and make it available within Jupyter:

conda activate weldx
python -m ipykernel install --user --name weldx --display-name "Python (weldx)"

This will enable us to select the Python interpreter installed in the weldx environment within Jupyter. So when a new notebook is created, we can choose Python (weldx) to access all the software bundled with the weldx Python package.

If you wish to setup multiple different kernels for Jupyter a guide can be found here.

Starting the Jupyer Lab

To start JupyterLab run:

conda activate jlab
jupyter lab

A window in your browser will automatically be opened.

Fixing DLL errors on Windows systems

In case you run into an error when using the weldx kernel on Windows systems that fails to read DLLs like:

ImportError: DLL load failed: The specified module could not be found

you might have to apply the fix mentioned here.

Go to %userprofile%\.ipython\profile_default\startup using the windows explorer and create a new file called ipython_startup.py. Open it with a text editor and paste the following commands into the file:

import sys
import os
from pathlib import Path

# get directory of virtual environment
p_env = Path(sys.executable).parent

# directory which should contain all the DLLs
p_dlls = p_env / "Library" / "bin"

# effectively prepend this DLL directory to $PATH
# semi-colon used here as sep on Windows
os.environ["PATH"] = "{};{}".format(p_dlls, os.environ["PATH"])

Everything in one-shot

If you feel lucky, you can try and copy-paste all install commands into a shell. Note that if one command fails, all subsequent commands will not be executed.

conda create --name weldx --channel conda-forge weldx weldx_widgets
conda create --name jlab jupyterlab k3d --channel conda-forge
conda activate weldx
python -m ipykernel install --user --name weldx --display-name "Python (weldx)"
conda activate jlab
jupyter lab