Displaying 1 to 16 from 16 results

xarray - N-D labeled arrays and datasets in Python

  •    Python

xarray (formerly xray) is an open source project and Python package that aims to bring the labeled data power of pandas to the physical sciences, by providing N-dimensional variants of the core pandas data structures. Our goal is to provide a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels. Our approach adopts the Common Data Model for self- describing scientific data in widespread use in the Earth sciences: xarray.Dataset is an in-memory representation of a netCDF file.

SDS: Scientific DataSet library and tools

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The SDS library makes it easy for .Net developers to read, write and share scalars, vectors, matrices and multidimensional grids which are very common in scientific modelling. It supports CSV, NetCDF and other file format

NetCDF library for .NET

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NetCDF (network Common Data Form) is a software library and a standard binary data format supported by Unidata (http://www.unidata.ucar.edu/software/netcdf/) that enables the creation, access, and network sharing of array-oriented scientific data. This project is dedicated to ...

netcdf4-python - netcdf4-python: python/numpy interface to the netCDF C library

  •    Python

Python/numpy interface to the netCDF C library. For details on the latest updates, see the Changelog.




clover - Geospatial operations for NetCDF and numpy

  •    Python

Because today might be your lucky day. Geospatial operations with NetCDF files and numpy arrays.

glider_toolbox - MATLAB/Octave scripts to manage data collected by a glider fleet, including data download, data processing and product and figure generation, both in real time and delayed time

  •    Matlab

The glider toolbox is a set of MATLAB/Octave scripts and functions developed at SOCIB to manage the data collected by a glider fleet. They cover the main stages of the data management process both in real time and delayed time mode: metadata aggregation, data download, data processing, and generation of data products and figures. The toolbox is exhaustively self-documented using the standard documentation comment system. Hence the help pages are available using the documentation browser or the help command.

go-netcdf - Go binding for the netCDF C library.

  •    Go

Package netcdf is a Go binding for the netCDF C library. This package supports netCDF version 3, and 4 if netCDF 4 support is enabled in the C library. First, make sure you have the netCDF C library is installed. Most Linux distributions have a package for it: libnetcdf-dev in Ubuntu/Debian, netcdf in ArchLinux, etc. You can also download the source from Unidata, compile and install it.

cdo-bindings - Ruby/Python bindings for CDO

  •    Python

Multi-dimensional arrays (numpy for python, narray for ruby) require addtional netcdf-io modules. These are scipy or python-netcdf4 for python and ruby-netcdf for ruby. Because scipy has some difficulties with netcdf, I strongly recommend python-netCDF4. Thx to Alexander Winkler there is also an IO option for XArray.


netcdf-c - Official GitHub repository for netCDF-C libraries and utilities.

  •    C

The Unidata network Common Data Form (netCDF) is an interface for scientific data access and a freely-distributed software library that provides an implementation of the interface. The netCDF library also 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 current netCDF software provides C interfaces for applications and data. Separate software distributions available from Unidata provide Java, Fortran, Python, and C++ interfaces. They have been tested on various common platforms. NetCDF files are self-describing, network-transparent, directly accessible, and extendible. Self-describing means that a netCDF file includes information about the data it contains. Network-transparent means that a netCDF file is represented in a form that can be accessed by computers with different ways of storing integers, characters, and floating-point numbers. Direct-access means that a small subset of a large dataset may be accessed efficiently, without first reading through all the preceding data. Extendible means that data can be appended to a netCDF dataset without copying it or redefining its structure.

siphon - Siphon - A collection of Python utilities for retrieving atmospheric and oceanic data from remote sources, focusing on being able to retrieve data from Unidata data technologies, such as the THREDDS data server

  •    Python

Siphon is a collection of Python utilities for downloading data from Unidata data technologies. See our support page for ways to get help with Siphon. Siphon is still in an early stage of development, and as such no APIs are considered stable. While we won't break things just for fun, many things may still change as we work through design issues.

thredds-docker - Dockerized THREDDS

  •    Shell

A containerized THREDDS Data Server built on top a security hardened Tomcat container maintained by Unidata. This project was initially developed by Axiom Data Science and now lives at Unidata. TDM Update: If you are looking for the TDM Docker container, it has moved into its own repository.

EC-netCDF-CF - EarthCube: Advancing netCDF-CF Project

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The project will gather scientific use-cases and example datasets to guide the drafting of documents detailing enhancements to the CF standard. Close collaboration with the existing netCDF-CF community and engagement with new geoscience domains as well as other standards groups will lead to strong community agreement around the proposed enhancements. See the new "Geometries" section of CF chapter 7 "Data Representative of Cells" GitHub Asciidoc here.

cftime - Time-handling functionality from netcdf4-python.

  •    Python

11/8/2016: cftime was split out of the netcdf4-python package. Clone GitHub repository (git clone https://github.com/Unidata/cftime.git), or get source tarball from PyPI. Links to Windows and OS X precompiled binary packages are also available on PyPI.

psyplot - Python package for interactive data visualization

  •    Python

Welcome! psyplot is an open source python project that mainly combines the plotting utilities of matplotlib and the data management of the xarray package. The main purpose is to have a framework that allows a fast, attractive, flexible, easily applicable, easily reproducible and especially an interactive visualization of your data. The ultimate goal is to help scientists and especially climate model developers in their daily work by providing a flexible visualization tool that can be enhanced by their own visualization scripts. psyplot can be used through the python command line and through the psyplot-gui module which provides a graphical user interface for an easier interactive usage.

netcdf4-js - NodeJS addon to read and write NetCDF4 files

  •    C++

NodeJS addon for reading and writing the files in the Network Common Data Form (NetCDF) version <= 4, built upon the C-library for netcdf. You will need libnetcdf >= 4.x installed.

geotrellis-netcdf - Scala/Spark Project For Reading NetCDF

  •    Scala

This repository contains an example project that demonstrates how to read NetCDF data from S3 or a local filesystem into a Spark/Scala program using NetCDF Java and manipulate the data using GeoTrellis. The ability to easily and efficiently read NetCDF data into a GeoTrellis program opens the possibility for those who are familiar with GeoTrellis and its related and surrounding tools to branch into climate research, and also makes it possible for climate researchers to take advantage of the many benefits that GeoTrellis can provide. Because GeoTrellis is a raster-oriented library, the approach that is demonstrated in this repository is to use the NetCDF library to load and query datasets and present the results as Java arrays which can be readily turned into GeoTrellis tiles. Once the data have been transformed into GeoTrellis tiles, they can be masked, summarized, and/or manipulated like any other GeoTrellis raster data. The results of that are shown below. In the last section there is a brief discussion of ideas for improving the S3 Reader.





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