Open Source Python Earth Sciences Software
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Python Earth Sciences Software
View 164 business solutionsBrowse free open source Python Earth Sciences Software and projects below. Use the toggles on the left to filter open source Python Earth Sciences Software by OS, license, language, programming language, and project status.
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Astropy
Repository for the Astropy core package
The Astropy Project is a community effort to develop a common core package for Astronomy in Python and foster an ecosystem of interoperable astronomy packages. Astropy is a Python library for use in astronomy. Learn Astropy provides a portal to all of the Astropy educational material through a single dynamically searchable web page. It allows you to filter tutorials by keywords, search for filters, and make search queries in tutorials and documentation simultaneously. The Anaconda Python Distribution includes Astropy and is the recommended way to install both Python and the Astropy package. The astropy package contains key functionality and common tools needed for performing astronomy and astrophysics with Python. It is at the core of the Astropy Project, which aims to enable the community to develop a robust ecosystem of affiliated packages covering a broad range of needs for astronomical research, data processing, and data analysis. -
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Madagascar is a software package for geophysical data processing and reproducible numerical experiments. The package mission is to provide a convenient environment for researchers working with digital image and data processing in geophysics and related fields.
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ALAMODE
Ab initio simulator for thermal transport and lattice anharmonicity
ALAMODE is designed for analyzing lattice anharmonicity and lattice thermal conductivity of solids. By using an external DFT package such as VASP and Quantum ESPRESSO, you can extract harmonic and anharmonic force constants straightforwardly with ALAMODE. Using the anharmonic force constants, you can also calculate lattice thermal conductivity from first principles. For more information about ALAMODE, please visit the following webpages: Documentation : http://alamode.readthedocs.io/en/latest/ git repository : https://github.com/ttadano/alamode -
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GPlates
Interactive visualization of plate tectonics.
GPlates is a plate-tectonics program. Manipulate reconstructions of geological and paleo-geographic features through geological time. Interactively visualize vector, raster and volume data. PyGPlates is the GPlates Python library. Get fine-grained access to GPlates functionality in your Python scripts. -
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DeepChem
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, etc
DeepChem aims to provide a high-quality open-source toolchain that democratizes the use of deep learning in drug discovery, materials science, quantum chemistry, and biology. DeepChem currently supports Python 3.7 through 3.9 and requires these packages on any condition. DeepChem has a number of "soft" requirements. If you face some errors like ImportError: This class requires XXXX, you may need to install some packages. Deepchem provides support for TensorFlow, PyTorch, JAX and each requires an individual pip Installation. The DeepChem project maintains an extensive collection of tutorials. All tutorials are designed to be run on Google collab (or locally if you prefer). Tutorials are arranged in a suggested learning sequence that will take you from beginner to proficient at molecular machine learning and computational biology more broadly. -
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GEOMS2
Geostatistics and geosciences modeling software
GEOMS2 is a geostatistics and geosciences modeling software. Provides interface for grid (mesh), point, surface and data (non-spatial) objects. It has a 3D viewer and 2D plots using the well known Python engines Mayavi and Matplotlib. It has several functions to manipulate your data as well as provide univariate and multivariate analysis. NOTE: The software is still an early beta. Please tell us if you found a bug. Download datasets for students of Geostatistics 2017 (Petroleum Engineering): https://sourceforge.net/projects/geoms2/files/Geostatistics_Petroleum.zip/download Download datasets for students of Geostatistics 2017 (Geology and Mining Engineering): https://sourceforge.net/projects/geoms2/files/Geostatistics_Mining.zip/download Old: http://sourceforge.net/projects/geoms2/files/SETS_geoestatistica2_2015.7z/download https://sites.google.com/site/cmrpsoftware/downloads/Quarry_sets.7z?attredirects=0&d=1 http://sourceforge.net/projects/geoms2/files/Mining.7z/download -
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GDAL wheels for linux
GDAL wheels for python and C/C++ projects (Linux only)
To use precompiled wheels: 1) go to releases (Files) and download tarball needed; 2) install it with command: python3 -m pip install /path/to/wheel.whl Or simply use URL in pip: python3 -m pip install https://sourceforge.net/projects/gdal-wheels-for-linux/files/GDAL-3.1.4-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl/download URL may be found under "View details" button (i) To use GDAL in C/C++ project you need to link gdal lib AND all libs located at dir GDAL.libs (usually this folder resides inside python site-packages) To compile your own wheels see information given at forefather project: https://github.com/youngpm/gdalmanylinux Usually this is done via command `make wheels` GDAL wheels for Windows are provided by Christoph Gohlke at https://www.lfd.uci.edu/~gohlke/pythonlibs/#gdal Built with PROJ (proj.db is included), GEOS, EXPAT. See Dockerfile.wheels for additional components. -
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Airborne Data Processing and Analysis
Software to processing and analyze of airborne measurements.
The Airborne Data Processing and Analysis (ADPAA) package is an open-source software package containing a collection of programs and scripts to process and analyze data from in-situ instruments deployed on airborne platforms. The ADPAA package was started to process data on the North Dakota Citation Research Aircraft but has been used to process data on many airborne platforms. The software methodology used in ADPAA is provided in the peer-review publication: Delene, D. J., Airborne Data Processing and Analysis Software Package, Earth Science Informatics, 4(1), 29-44, 2011, URL: http://dx.doi.org/10.1007/s12145-010-0061-4, DOI: 10.1007/s12145-010-0061-4. -
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Cca-forum unifies the Common Component Architecture tools and tutorial. It includes the CCA specifications, the Ccaffeine framework for HPC, and related tools. These support multilanguage scientific and parallel computing.
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CAIRO for AERMOD
AERMOD, visualisation, input, modelling and compiling tool
CAIRO for AERMOD v1.1 by MSc Dominik Subotić Simplified training software Avaliable: www.sourceforge.net/projects/cairo-for-aermod/ QGIS plugin: CAIROforAERMOD (Coming 2025.) Tutorial: https://www.youtube.com/watch?v=DZnsJuu1zLc AERMAP, AERMOD and AERPLOT analysis tool and input file compiler. Features: Automatic input by copying coordinates (Google Maps or text) and automatic conversion to UTM. Sources are automatically visualised in Google Earth. Input is done through user interface and automatically compiled into correct syntax and project/file structure. Windows application, supports up to 3 averaging periods, maxtable, maxifile, rankfile and plotfile. Elevation and meteorological data is third party. CAIRO for AERMOD (Compile AERMAP, AERMOD and AERPLOT Input and Run Output) aimed at streamlining the process of making complex input file, with unique syntax and running them. While obviating the need to manually write inputs and running the program through Shell -
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UV-CDAT
Ultrascale Visualization Climate Data Analysis Tools
Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT), is a powerful and complete front-end to a rich set of visual-data exploration and analysis capabilities well suited for climate-data analysis problems. -
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jif3d
3D Joint Inversion Framework
jif3d is a framework for joint inversion of different types of geophysical data in 3D. It currently comprises forward solvers for first arrival traveltimes, scalar and tensor gravity data, magnetics, magnetotellurics, electrical resistivity tomography and surface waves.The framework is flexible in terms of the coupling approach between the different datasets. At the moment cross-gradient coupling, variation of information and deterministic parameter relationships are implemented. Other coupling approaches can be added relatively easily. -
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MOSSCO
Modular System for Shelves and Coasts
The Modular System for Shelves and Coasts (MOSSCO) is a coupling framework for Earth System Models. It helps users to integrate their own numerical models with other developments. Quick Start: git clone git://git.code.sf.net/p/mossco/setups mossco-setups git clone git://git.code.sf.net/p/mossco/code mossco-code cd mossco-code cat QuickStart.md or read online https://sf.net/p/mossco/code/ci/master/tree/QuickStart.md Beware: this is alpha software, expect trouble! ------------------------- Better, read the full documentation online http://www.mossco.de/doc or make it yourself make doc acroread doc/mossco_documentation.pdf ---------------------- Why MOSSCO? MOSSCO, the "Modular System for Shelves and Coasts" is a framework for coupling processes or domains that are originally developed in standalone numerical models. The software MOSSCO implements this infrastructure in the form of a library of components and couplers, and of example coupled application. -
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This application provides a web based visual browser for NetCDF files. The application provides a simple and friendly user interface for getting a quick overview of the data contained in the files.
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Spectral Python
A python module for hyperspectral image processing
Spectral Python (SPy) is a python package for reading, viewing, manipulating, and classifying hyperspectral image (HSI) data. SPy includes functions for clustering, dimensionality reduction, supervised classification, and more. -
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A python-based system to diagnosis and make weather maps (wxmap) of numerical weather prediction models using the Grid Analysis and Display System (GrADS) from opengrads.org
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WARNING! HPGL source code (NOT the releases, they are still here) now moved to the GitHub at https://github.com/hpgl/hpgl. Please, update your bookmarks!
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openGPR is a framework for the management, processing and 2D/3D-visualization of ground penetrating radar (GPR, georadar) data. It interoperates with existing tools like Seismic Unix, with a special focus on georadar applications.
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Engineering, analysis and computing tools for marine and coastal science and technology
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PySptools
Hyperspectral algorithms for Python
A lightweight hyperspectral imaging library that provides developers with spectral algorithms for the Python programming language. New for v0.14.x: a scikit-learn bridge (alpha and partial). The functions and classes are organized by topics: * abundance maps: FCLS, NNLS, UCLS * classification: AbundanceClassification, NormXCorr, KMeans SAM, SID, SVC * detection: ACE, CEM, GLRT, MatchedFilter, OSP * distance: chebychev, NormXCorr, SAM, SID * endmembers extraction: ATGP, FIPPI, NFINDR, PPI * material count: HfcVd, HySime * noise: Savitzky Golay, MNF, whiten * sigproc: bilateral * sklearn: HyperEstimatorCrossVal, HyperSVC and others * spectro: convex hull quotient, features extraction (tetracorder style), USGS06 lib interface * util: load_ENVI_file, load_ENVI_spec_lib, corr, cov and others The library do an extensive use of the numpy numeric library and can achieve good speed. The library is mature enough and is very usable even if the development is at a beta step. -
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GrainSizeTools script
A Python script for estimating the grain size from thin sections
Homepage & docs: http://marcoalopez.github.io/GrainSizeTools/ GrainSizeTools is a free, open-source, cross-platform script written in Python that provides several tools for (1) estimating average grain size in polycrystalline materials, (2) characterizing the nature of the distribution of grain sizes (either from apparent distributions or approximating 3D grain size distributions via stereology), and estimating differential stress via paleopizometers. The script requires as the input the areas of the grain profiles measured grain-by-grain on planar sections and does not require previous experience with Python programming language (see documentation below and FAQ). For users with coding skills, the script is organized in a modular way facilitating the reuse and code extension. Lopez-Sanchez, MA (2018). GrainSizeTools: a Python script for grain size analysis and paleopiezometry based on grain size. Journal of Open Source Software, 3(30), 863, https://doi.org/10.21105/joss.00863 -
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ESML is an interchange technology that utilizes external metadata to allow applications to plug and play seamlessly with Earth Science specific datasets in heterogeneous formats.
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The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.
