The electromagnetic modeller empymod can model electric or magnetic responses due to a three-dimensional electric or magnetic source in a layered-earth model with vertical transverse isotropic (VTI) resistivity, VTI electric permittivity, and VTI magnetic permeability, from very low frequencies (DC) to very high frequencies (GPR). The computation is carried out in the wavenumber-frequency domain, and various Hankel- and Fourier-transform methods are included to transform the responses into the space-frequency and space-time domains.

PyGMT is a library for processing geospatial and geophysical data and making publication-quality maps and figures. It provides a Pythonic interface for the Generic Mapping Tools (GMT), a command-line program widely used across the Earth, Ocean, and Planetary sciences and beyond.

This package provides programmatic access to the data store catalogue of the Copernicus CDS.

pyKML is a Python package for creating, parsing, manipulating, and validating KML, a language for encoding and annotating geographic data.

This package provides conversion functions between UTM and WGS84 coordinates.

This packge provides utilities to retrieve tile maps from the internet. It can add those tiles as basemap to matplotlib figures or write tile maps to disk into geospatial raster files. Bounding boxes can be passed in both WGS84 (EPSG:4326) and Spheric Mercator (EPSG:3857).

This package provides a geospatial extension for xarray powered by rasterio.

Choclo is a Python library that hosts optimized kernel functions for running geophysical forward and inverse models, intended to be used by other libraries as the underlying layer of their computation.

This package provides a set of sparse matrix solvers used in geoscience.

All solvers work with scipy.sparse matricies, and a single or multiple right hand sides using numpy:

• L/U Triangular Solves

• Wrapping of SciPy matrix solvers (direct and indirect)

• Pardiso solvers now that MKL comes with conda!

• Mumps solver with nice error messages

Provides a high-level interface to the ECWMF ECCODES C library for reading GRIB files. There are limited capabilities for writing GRIB files (you can modify the contents of an existing file, but you can't create one from scratch).

This package provides a Python library for manipulation and storage of a wide range of geoscientific data (points, curve, surface, 2D and 3D grids) in *.geoh5 file format.

This package provides a Python wrapper for solid to compute solid Earth tides.

NetCDF is an interface for scientific data access and a software library that provides an implementation of the interface. The netCDF library defines a machine-independent format for representing scientific data. Together, the interface, library, and format support the creation, access, and sharing of scientific data.

The Model for Prediction Across Scales - Atmosphere (MPAS-A) is a n-hydrostatic atmosphere model that is part of a family of Earth-system mponent models collectively known as MPAS. All MPAS models have in common eir use of centroidal Voronoi tessellations for their horizontal meshes, ich has motivated the development of a common software framework that ovides a high-level driver program and infrastructure for providing rallel execution, input and output, and other software infrastructure.

This package an alternative qsub command that will submit jobs to SLURM.

The Son of Grid Engine is a community project to continue Sun's old gridengine project after Oracle shut down the site and stopped contributing code.

This package an alternative qsub command that will directory run the script.

ADIOS2 transports data as groups of self-describing variables and attributes across different media types (such as files, wide-area-networks, and remote direct memory access) using a common application programming interface for all transport modes. ADIOS2 can be used on supercomputers, cloud systems, and personal computers.

IOR is a parallel IO benchmark that can be used to test the performance of parallel storage systems using various interfaces and access patterns. The IOR repository also includes the mdtest benchmark which specifically tests the peak metadata rates of storage systems under different directory structures. Both benchmarks use a common parallel I/O abstraction backend and rely on MPI for synchronization.

PaRSEC is a generic framework for architecture aware scheduling and management of micro-tasks on distributed many-core heterogeneous architectures. Applications are expressed as a Direct Acyclic Graph of tasks with labeled edges designating data dependencies. PaRSEC assigns computation threads to the cores, overlaps communications and computations between nodes as well as between host and accelerators (like GPUs). It achieves these features by using a dynamic, fully-distributed scheduler based on architectural features such as NUMA nodes and GPU awareness, as well as algorithmic features such as data reuse.

StarPU is a run-time system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible.

StarPU is a run-time system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible.

StarPU is a run-time system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible.

StarPU is a run-time system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible.