atmosphere   1456

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Carbon Lab | The Habitable Planet, Annenberg Media
simulation shows how carbon can move around a habitat (sea, air, trees) through fossil fuel use, etc.
science  chemistry  geology  atmosphere  climate_change 
19 days ago by MsHsi
Global Warming
Eric R. Pianka, U Texas at Austin. Tons of interesting links
science  climate_change  atmosphere 
19 days ago by MsHsi
Carbon Cycle | NOAA
homepage for series of links about the carbon cycle
science  chemistry  geology  atmosphere  climate_change 
19 days ago by MsHsi
Capturing Carbon: Can it Save Us? | C&ENews
would "scrubbing" CO2 at factory sources be enough to prevent climate change, and is it worth the industry dollars?
science  chemistry  physics  geology  climate_change  atmosphere 
19 days ago by MsHsi
hvPlot 0.4.0 documentation
A high-level plotting API for the PyData ecosystem built on HoloViews.

The PyData ecosystem has a number of core Python data containers that allow users to work with a wide array of datatypes, including:

Pandas : DataFrame, Series (columnar/tabular data)
XArray : Dataset, DataArray (labelled multidimensional arrays)
Dask : DataFrame, Series (distributed/out of core arrays and columnar data)
Streamz : DataFrame(s), Series(s) (streaming columnar data)
Intake : DataSource (data catalogues)
GeoPandas : GeoDataFrame (geometry data)
NetworkX : Graph (network graphs)
Several of these libraries have the concept of a high-level plotting API that lets a user generate common plot types very easily. The native plotting APIs are generally built on Matplotlib , which provides a solid foundation, but it means that users miss out on the benefits of modern, interactive plotting libraries for the web like Bokeh and HoloViews .

hvPlot provides a high-level plotting API built on HoloViews that provides a general and consistent API for plotting data in all the abovementioned formats. hvPlot can integrate neatly with the individual libraries if an extension mechanism for the native plot APIs is offered, or it can be used as a standalone component. To get started jump straight into the Getting Started Guide and check out the current functionality in the User Guide.
python  visualization  planetary  atmosphere 
5 weeks ago by soto97
Interactive data visualization with python — psyplot 1.2.0 documentation
Welcome! Looking for a fast and flexible visualization software? Here we present psyplot, an open source python project that mainly combines the plotting utilities of matplotlib and the data management of the xarray package and integrates them into a software that can be used via command-line and via a GUI!

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 in their daily work by providing a flexible visualization tool that can be enhanced by their own visualization scripts. psyplot can be used via command line and with the graphical user interface (GUI) from the psyplot-gui module.
python  visualization  planetary  atmosphere 
5 weeks ago by soto97
wrf-python — wrf-python 1.3.1 documentation
A collection of diagnostic and interpolation routines for use with output from the Weather Research and Forecasting (WRF-ARW) Model.

This package provides over 30 diagnostic calculations, several interpolation routines, and utilities to help with plotting via cartopy, basemap, or PyNGL. The functionality is similar to what is provided by the NCL WRF package.

When coupled with either matplotlib or PyNGL, users can create plots like this:
python  wrf  visualization  planetary  science  atmosphere  climate 
5 weeks ago by soto97
Iris: A powerful, format-agnostic, community-driven Python library for analysing and visualising Earth science data.
Iris implements a data model based on the CF conventions giving you a powerful, format-agnostic interface for working with your data. It excels when working with multi-dimensional Earth Science data, where tabular representations become unwieldy and inefficient.

CF Standard names, units, and coordinate metadata are built into Iris, giving you a rich and expressive interface for maintaining an accurate representation of your data. Its treatment of data and associated metadata as first-class objects includes:

a visualisation interface based on matplotlib and cartopy,
unit conversion,
subsetting and extraction,
merge and concatenate,
aggregations and reductions (including min, max, mean and weighted averages),
interpolation and regridding (including nearest-neighbor, linear and area-weighted), and
operator overloads (+, -, *, /, etc.).
A number of file formats are recognised by Iris, including CF-compliant NetCDF, GRIB, and PP, and it has a plugin architecture to allow other formats to be added seamlessly.
python  visualization  planetary  atmosphere 
5 weeks ago by soto97
Pangeo — Pangeo documentation
A community platform for Big Data geoscience

Our Goals
Foster collaboration around the open source scientific python ecosystem for ocean / atmosphere / land / climate science.
Support the development with domain-specific geoscience packages.
Improve scalability of these tools to handle petabyte-scale datasets on HPC and cloud platforms.
python  science  planetary  atmosphere  visualization 
5 weeks ago by soto97

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