Skip to main content

NC² NetCDF viewer

Project description

Welcome To NC²

What are NetCDF Files?

NetCDF (Network Common Data Form) is a set of software libraries and self-describing, machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data. NetCDF files are widely used in climate science, meteorology, oceanography, and other fields to store and distribute large datasets. These files are valuable for their ability to store multi-dimensional data (such as temperature, humidity, and wind speed) efficiently, making them essential for researchers and scientists dealing with complex environmental data.

NCview and Panoply

Programs such as NCview and Panoply have become indispensable tools for scientists working with NetCDF files.

NCview: NCview is a simple yet powerful visual browser for NetCDF files. It allows users to quickly view the data stored in these files through various plotting options. NCview is particularly useful for scientists who need to quickly check the contents of their datasets.

Panoply: Panoply, developed by NASA, is another powerful tool that provides more advanced features than NCview. It allows users to visualize georeferenced and other arrays from NetCDF, HDF, and GRIB datasets. Panoply offers a range of plotting options, including latitude-longitude, latitude-vertical, and time-latitude plots, making it a versatile tool for in-depth data analysis.

Both NCview and Panoply are essential for scientists who need to visualize and analyze their data quickly. However, these tools come with limitations, such as less intuitive user interfaces and a lack of customization options for publication-quality plots.

What is NC²?

Logo4

NC² is a next-generation NetCDF viewer designed to overcome the limitations of traditional tools like NCview and Panoply. Built with modern scientific Python tools such as CartoPy and Matplotlib, NC² offers a versatile and user-friendly platform for visualizing and analyzing NetCDF data. Here’s what NC² brings to the table:

Modern GUI: NC² features a clean and intuitive graphical user interface (GUI) that significantly reduces the learning curve for new users. 

Familiar Tools: NC² is built with familiar scientific Python libraries, including CartoPy for cartographic projections and Matplotlib for plotting. This makes it easy for users who are already familiar with these tools to extend and customize their data visualizations.

Publication-Quality Plots: One of the standout features of NC² is its ability to produce publication-quality plots. Users can create highly customizable plots that meet standards required for scientific publications, ensuring that their visualizations are both accurate and aesthetically pleasing.

Versatility: NC² is designed to handle a wide range of NetCDF data types. Whether you are working with atmospheric data, oceanographic measurements, or climate model outputs, NC² provides the tools you need to visualize and analyze your data effectively.

Easy Customization: NC² offers a variety of customization options, allowing users to tailor their plots to their specific needs. From adjusting color maps and scales to adding annotations and labels, NC² makes it easy to create detailed and informative visualizations.

Advanced Features: In addition to basic plotting functionalities, NC² provides advanced features such as time series analysis, depth profiling, and GIF generations. These features enable users to gain deeper insights into their data and conduct more comprehensive analyses.

Explore the features and capabilities of NC², and see how it can enhance your research and data analysis workflows.

Screenshot from 2024-07-23 22-41-41

Contact Me!

rhettadambusiness@gmail.com

Rhett R. Adam 7/25/24

VU Undergrad EES

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nc2-0.1.0.tar.gz (37.8 kB view details)

Uploaded Source

Built Distribution

nc2-0.1.0-py3-none-any.whl (34.2 kB view details)

Uploaded Python 3

File details

Details for the file nc2-0.1.0.tar.gz.

File metadata

  • Download URL: nc2-0.1.0.tar.gz
  • Upload date:
  • Size: 37.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.7

File hashes

Hashes for nc2-0.1.0.tar.gz
Algorithm Hash digest
SHA256 ff9e5ddfe7784366129fb8fdd95f495a914533267268e018cf451e8cdbcbf43d
MD5 3c711c2df8f8b22a2c3be543070d4e67
BLAKE2b-256 9fef852d0d3b4c87c897bbc8f6a39fa4a4c00160c604565e05cfd7280ef1d72c

See more details on using hashes here.

File details

Details for the file nc2-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: nc2-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 34.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.7

File hashes

Hashes for nc2-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 bb5e0ce0bd47b071df9315d47236f69d39554a7eec24711a298c0e5edd1c6c2e
MD5 1a3fc60984abaa5fe12ab0b6b5d798ef
BLAKE2b-256 96461af987ed6427d671d9af2a4d0518820ac9b5cd0fdbb8e541594fae73c530

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page