scipion-ed 3!1.0.0

Base pyworkflow plugin for Electron Diffraction image processing

scipion-ed is the base plugin defining the Domain for Electron Diffraction image processing.

Installation

For using scipion-ed, we need to have a working Python 3 environment (e.g via virtualenv or conda). After that, we might easily install by:

pip install scipion-ed scipion-ed-dials

Development

For development, we probably want to download the source code and install from there. In that way changes can be made and we can test it quickly. We also need a working Python 3 environment and we recommend to create a development folder to download the source code.

# Create a folder for the installation
mkdir scipion-ed-dev
cd scipion-ed-dev

# Then we can install scipion-ed by:
git clone [e-mail]:scipion-ed/scipion-ed.git
python -m pip install -e scipion-ed  # Install in the environment as development

# And also install the plugins
git clone [e-mail]:scipion-ed/scipion-ed-dials.git
python -m pip install -e scipion-ed-dials  # Install in the environment as development

Publishing the package to PyPI

In order to make scipion-ed available to install with pip install scipion-ed, we need to:

python install twine restructuredtext-lint
cd scipion-ed

# It might be a good idea to check the README.rst before uploading:
rst-lint README.rst

python setup.py sdist
twine upload dist/scipion-ed-3.0.1.tar.gz

Running tests (TO BE UPDATED)

cd scipion-ed
cd pwed/tests
python -m unittest discover

# To visualize the test project you need to specify SCIPION_DOMAIN and SCIPION_VERSION
export SCIPION_DOMAIN=scipion-ed/pwed
export SCIPION_VERSION=3.0.0

python scipion-pyworkflow/pyworkflow/apps/pw_project.py TestEdBaseProtocols

Python 3 environments

For development, we probably want to download the source code and install from there. In that way changes can be made and we can test it quickly.

We also need a working Python 3 environment and we recommend to create a development folder to download the source code.

# Create a clean virtual environment
python -m virtualenv --python=python3 env
source env/bin/activate

It is also possible to use a conda environment.

# Create the environment
conda create -n sped-dev python=3.8 pip
conda activate sped-dev

Troubleshooting

If you get “error: command ‘x86_64-linux-gnu-gcc’ failed with exit status 1” you may need to install python3-dev: sudo apt install python3-dev -y

Tkinter with Python3

Tkinter with Conda