Displaying 1 to 20 from 22 results

satpy - Python package for earth-observing satellite data processing

  •    Python

The Satpy package is a python library for reading and manipulating meteorological remote sensing data and writing it to various image and data file formats. Satpy comes with the ability to make various RGB composites directly from satellite instrument channel data or higher level processing output. The pyresample package is used to resample data to different uniform areas or grids. The documentation is available at http://satpy.readthedocs.org/.

py-gdx - A Pythonic interface to GAMS GDX files

  •    Python

PyGDX is a Python package for accessing data stored in GAMS Data eXchange (GDX) files. GDX is a proprietary, binary file format used by the General Algebraic Modelling System (GAMS); pyGDX uses the Python bindings for the GDX API. Originally inspired by the similar package, also named py-gdx, by Geoff Leyland, this version makes use of xarray to provide labelled data structures which can be easily manipulated with NumPy for calculations and plotting.

spark-xarray - This is an experimental project that seeks to integrate PySpark and xarray for Climate Data Analysis

  •    Jupyter

spark-xarray is an open source project and Python package that seeks to integrate PySpark and xarray for Climate Data Analysis. It is built on top of PySpark - Spark Python API and xarray. spark-xarray was originally conceived during the Summer of 2017 as part of PySpark for "Big" Atmospheric & Oceanic Data Analysis - A CISL/SIParCS Research Project.

georinex - Python RINEX 2/3 NAV/OBS reader / batch conversion to HDF5 with C-like speed using xarray

  •    Python

RINEX 3 and RINEX 2 reader and batch conversion to NetCDF4 / HDF5 in Python or Matlab. Batch converts NAV and OBS GPS RINEX data into xarray.Dataset for easy use in analysis and plotting. This gives remarkable speed vs. legacy iterative methods, and allows for HPC / out-of-core operations on massive amounts of GNSS data. GeoRinex works in Python ≥ 3.6. Pure compiled language RINEX processors such as within Fortran NAPEOS give perhaps 2x faster performance than this Python program--that's pretty good for a scripted language like Python! However, the initial goal of this Python program was to be for one-time offline conversion of ASCII (and compressed ASCII) RINEX to HDF5/NetCDF4, where ease of cross-platform install and correctness are primary goals.




gcpy - Python toolkit for GEOS-Chem.

  •    Python

GCPy is a Python-based toolkit containing useful functions for working specifically with the GEOS-Chem model of atmospheric chemistry and composition. GCPy aims to build on the well-established scientific Python technical stack, leveraging tools like cartopy and xarray to simplify the task of working with model output and performing atmospheric chemistry analyses.

GEOSChem-python-tutorial - Python/xarray tutorial for GEOS-Chem users

  •    Jupyter

Click here to launch a pre-configured notebook environment on the cloud platform provided freely by the binder project. Use the Chrome browser if you have trouble loading that page. Refresh the page if loading fails. If the page is loaded successfully, you should see a Jupyter notebook interface. Then, click on the first notebook to get started. Jupyter combines Python code, execution results, plots, custom texts, and even Latex formulas in a single page. Besides using the Jupyter program, you can also view the static notebook on GitHub (e.g the first notebook).

synthia - 📈 🐍 Multidimensional synthetic data generation in Python

  •    Python

Synthetic data need to preserve the statistical properties of real data in terms of their individual behavior and (inter-)dependences (Meyer et al. 2021). Copula and functional Principle Component Analysis (fPCA) are statistical models that allow these properties to be simulated (Joe 2014). As such, copula generated data have shown potential to improve the generalization of machine learning (ML) emulators (Meyer et al. 2021) or anonymize real-data datasets (Patki et al. 2016). Synthia is an open source Python package to model univariate and multivariate data, parameterize data using empirical and parametric methods, and manipulate marginal distributions. It is designed to enable scientists and practitioners to handle labelled multivariate data typical of computational sciences. For example, given some vertical profiles of atmospheric temperature, we can use Synthia to generate new but statistically similar profiles in just three lines of code (Table 1).

xclim - Library of derived climate variables, ie climate indicators, based on xarray.

  •    Python

xclim is a library of functions to compute climate indices from observations or model simulations. It is built using xarray and can benefit from the parallelization handling provided by dask. Its objective is to make it as simple as possible for users to compute indices from large climate datasets and for scientists to write new indices with very little boilerplate. For applications where meta-data and missing values are important to get right, xclim provides a class for each index that validates inputs, checks for missing values, converts units and assigns metadata attributes to the output. This also provides a mechanism for users to customize the indices to their own specifications and preferences.


cesm-lens-aws - Examples of analysis of CESM LENS data publicly available on Amazon S3 (us-west-2 region) using xarray and dask

  •    Jupyter

Examples of analysis of CESM LENS data publicly available on Amazon S3 (us-west-2 region) using xarray and dask. This catalog is an ESM collection catalog. The data is stored in Zarr format and meant to be opened with Xarray.

esmlab - Earth System Model Lab (esmlab)

  •    Python

Tools for working with earth system multi-model analyses with xarray. See documentation for more information.

ncar-python-tutorial - Numerical & Scientific Computing with Python Tutorial

  •    Jupyter

NOTE: For windows users, setup scripts provided in this repository don't work on Windows machines for the time being. NOTE: Be prepared for the script to take up to 15 minutes to complete.

pyresample - Geospatial image resampling in Python

  •    Python

Pyresample is a python package for resampling geospatial image data. It is the primary method for resampling in the Satpy library, but can also be used as a standalone library. Resampling or reprojection is the process of mapping input geolocated data points to a new target geographic projection and area. Pyresample can operate on both fixed grids of data and geolocated swath data. To describe these data Pyresample uses various "geometry" objects including the AreaDefinition and SwathDefinition classes.

aospy - Python package for automated analysis and management of gridded climate data

  •    Python

aospy is a Python-based tool for automating computations involving gridded climate data and the management of the results of those computations. Use it to accelerate your science by automating your climate data workflow. And that's it! We're also available via pip: pip install aospy. Then checkout the official documentation for instructions on how to get started.

georinex - Python RINEX 2 / 3 NAV / OBS / sp3 reader & batch convert to HDF5 with C-like speed

  •    Python

RINEX 3 and RINEX 2 reader and batch conversion to NetCDF4 / HDF5 in Python or Matlab. Batch converts NAV and OBS GPS RINEX (including Hatanaka compressed OBS) data into xarray.Dataset for easy use in analysis and plotting. This gives remarkable speed vs. legacy iterative methods, and allows for HPC / out-of-core operations on massive amounts of GNSS data. GeoRinex works in Python ≥ 3.6 and has over 125 unit tests driven by Pytest. Pure compiled language RINEX processors such as within Fortran NAPEOS give perhaps 2x faster performance than this Python program--that's pretty good for a scripted language like Python! However, the initial goal of this Python program was to be for one-time offline conversion of ASCII (and compressed ASCII) RINEX to HDF5/NetCDF4, where ease of cross-platform install and correctness are primary goals.

cupy-xarray - Interface for using cupy in xarray, providing convenience accessors.

  •    Python

Interface for using cupy in xarray, providing convenience accessors.

xarray-simlab - Xarray extension and framework for computer model simulations

  •    Python

xarray-simlab is a Python library that provides both a generic framework for building computational models in a modular fashion and a xarray extension for setting and running simulations using the xarray's Dataset structure. It is designed for fast, interactive and exploratory modeling. xarray-simlab is well integrated with other libraries of the PyData ecosystem such as dask and zarr.

xoak - xoak is an xarray extension that provides tree-based indexes used for selecting irregular, n-dimensional data

  •    Python

Xoak is an Xarray extension that allows point-wise selection of irregular, n-dimensional data encoded in coordinates with an arbitrary number of dimensions. Xoak also provides a mechanism for easily adding and registering custom index adapters.

xpublish - Publish Xarray Datasets via a REST API.

  •    Python

Publish Xarray Datasets via a REST API. Xpublish lets you serve/share/publish Xarray Datasets via a web application.

climpred - :earth_americas: Verification of weather and climate forecasts. :earth_africa:

  •    Python

Verification of weather and climate forecasts. We are actively looking for new contributors for climpred! Riley moved to McKinsey's Climate Analytics team. Aaron is finishing his PhD, but will stay in academia. We especially hope for python enthusiasts from seasonal, subseasonal or weather prediction community. In our past coding journey, collaborative coding, feedbacking issues and pull requests advanced our code and thinking about forecast verification more than we could have ever expected. Aaron can provide guidance on implementing new features into climpred. Feel free to implement your own new feature or take a look at the good first issue tag in the issues. Please reach out to us via gitter.






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