P

python

A python package to analyse LSM and DGVM outputs

Set of tools to harvest, process and uplift (meta)data from metadata providers within the Helmholtz association to be included in the Helmholtz Knowledge Graph (Helmholtz-KG). The harvested linked data in the form of schema.org jsonld is aggregated and uplifted in data pipelines to be included into a single large knowledge graph (KG). The tool set and harvesters can be used as a python library or over a commandline interface (CLI, hmc-unhide). Provenance of metadata changes is tracked rudimentary by saving graph patches of changes on rdflib Graph data structures on the semantic triple level. Harvesters support extracting data via sitemap, gitlab API, datacite API and OAI-PMH endpoints.

Source code for Multiphase Python Repository by HZDR

A framework leveraging the Functional Mock-up Interface (FMI) to train, validate, and assess Deep Reinforcement Learning agents in complex multi-physical environments.

COSmic ray based soil MOisture PredictiOn LIve Tree ANalysis

A quick introduction to the data science framework pandas.

Tree Crown Segmentation and Analysis in Remote Sensing Imagery with PyTorch -- https://deeptrees.de

A Python Ecosystem for Harvesting Datasets information from Amazon S3 via INTAKE catalog and Cultivating STAC-Metadata.

Please have a look at https://intake2stac.readthedocs.io for the documentation.

Ultralytics YOLO with DEEPaaS API

A Python Ecosystem for Harvesting Time Series data information from SensorthingsAPI (STA) and Cultivating STAC-Metadata.

Please have a look at https://sta2stac.readthedocs.io/ for the documentation.

Terminal-based Python scripts to automate processing and classifying of xylem cavitation and optical dendrometry image sequences.

WDCC-compliant NetCDF Creation Example in Python for HMG NetCDF chapter https://hmg-netcdf.readthedocs.io/en/v1.0.0/examples/example-wdcc-minimal-standard.html

This PAS is a GUI-based collection of tools that helps users discover and run Python scripts from a folder and its subfolders. It provides a convenient way to execute Python and Pythonw files directly from a graphical interface, with additional features like automatic library installation and organized program listing. Included are scripts to analyze different data types.

Mirror of https://gitlab.cern.ch/geoff/geoff-app/

Mirror of https://gitlab.cern.ch/geoff/cernml-coi/

Mirror of https://gitlab.cern.ch/geoff/cernml-coi-utils/

Mirror of https://gitlab.cern.ch/geoff/cernml-coi-optimizers/