AiiDA plugins and work chains developed at nanotech@surfaces group from Empa.
Project description
aiida-nanotech-empa
AiiDA plugin containing plugins/work chains developed at nanotech@surfaces group from Empa.
This plugin is the default output of the AiiDA plugin cutter, intended to help developers get started with their AiiDA plugins.
Repository contents
.github/
: Github Actions configurationci.yml
: runs tests, checks test coverage and builds documentation at every new commitpublish-on-pypi.yml
: automatically deploy git tags to PyPI - just generate a PyPI API token for your PyPI account and add it to thepypi_token
secret of your github repository
aiida_nanotech_empa/
: The main source code of the plugin packagedata/
: A newDiffParameters
data class, used as input to theDiffCalculation
CalcJob
classcalculations.py
: A newDiffCalculation
CalcJob
classcli.py
: Extensions of theverdi data
command line interface for theDiffParameters
classhelpers.py
: Helpers for setting up an AiiDA code fordiff
automaticallyparsers.py
: A newParser
for theDiffCalculation
docs/
: A documentation template ready for publication on Read the Docsexamples/
: An example of how to submit a calculation using this plugintests/
: Basic regression tests using the pytest framework (submitting a calculation, ...). Installpip install -e .[testing]
and runpytest
..coveragerc
: Configuration of coverage.py tool reporting which lines of your plugin are covered by tests.gitignore
: Telling git which files to ignore.pre-commit-config.yaml
: Configuration of pre-commit hooks that sanitize coding style and check for syntax errors. Enable viapip install -e .[pre-commit] && pre-commit install
.readthedocs.yml
: Configuration of documentation build for Read the DocsLICENSE
: License for your pluginMANIFEST.in
: Configure non-Python files to be included for publication on PyPIREADME.md
: This fileconftest.py
: Configuration of fixtures for pytestpytest.ini
: Configuration of pytest test discoverysetup.json
: Plugin metadata for registration on PyPI and the AiiDA plugin registry (including entry points)setup.py
: Installation script for pip / PyPI
For more information, see the developer guide of your plugin.
Features
-
Add input files using
SinglefileData
:SinglefileData = DataFactory('singlefile') inputs['file1'] = SinglefileData(file='/path/to/file1') inputs['file2'] = SinglefileData(file='/path/to/file2')
-
Specify command line options via a python dictionary and
DiffParameters
:d = { 'ignore-case': True } DiffParameters = DataFactory('nanotech_empa') inputs['parameters'] = DiffParameters(dict=d)
-
DiffParameters
dictionaries are validated using voluptuous. Find out about supported options:DiffParameters = DataFactory('nanotech_empa') print(DiffParameters.schema.schema)
Installation
pip install aiida-nanotech-empa
verdi quicksetup # better to set up a new profile
verdi plugin list aiida.calculations # should now show your calclulation plugins
Usage
Here goes a complete example of how to submit a test calculation using this plugin.
A quick demo of how to submit a calculation:
verdi daemon start # make sure the daemon is running
cd examples
./example_01.py # run test calculation
verdi process list -a # check record of calculation
The plugin also includes verdi commands to inspect its data types:
verdi data nanotech_empa list
verdi data nanotech_empa export <PK>
Development
git clone https://github.com/yakutovicha/aiida-nanotech-empa .
cd aiida-nanotech-empa
pip install -e .[pre-commit,testing] # install extra dependencies
pre-commit install # install pre-commit hooks
pytest -v # discover and run all tests
See the developer guide for more information.
License
MIT
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file aiida-nanotech-empa-0.1.0a1.tar.gz
.
File metadata
- Download URL: aiida-nanotech-empa-0.1.0a1.tar.gz
- Upload date:
- Size: 16.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.7.4
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 098da4a4ee92b5a26af0d9db841942767dd3f272e90509e6c3656bc373c74e24 |
|
MD5 | 42ec2ecebb2575a38489ae2bf9c3a979 |
|
BLAKE2b-256 | f9daf2224e238f8f9b04090ef2331531e65da7bca20bb7d3ebb17f0977fef4fb |