The psyplot interactive visualization framework

psyplot is an cross-platform 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

STRADITIZE: An open-source program for digitizing pollen diagrams and other types of stratigraphic data

In an age of digital data analysis, gaining access to data from the pre-digital era – or any data that is only available as a figure on a page – remains a problem and an under-utilized scientific resource. Whilst there are numerous programs available that allow the digitization of scientific data in a simple x-y graph format, we know of no semi-automated program that can deal with data plotted with multiple horizontal axes that share the same vertical axis, such as pollen diagrams and other stratigraphic figures that are common in the Earth sciences. STRADITIZE (Stratigraphic Diagram Digitizer) is a new open-source program that allows stratigraphic figures to be digitized in a single semi-automated operation. It is designed to detect multiple plots of variables analyzed along the same vertical axis, whether this is a sediment core or any similar depth/time series. The program is written in python and supports mixtures of many different diagram types, such as bar plots, line plots, as well as shaded, stacked, and filled area plots. The package provides an extensively documented graphical user interface for a point-and-click handling of the semi-automatic process, but can also be scripted or used from the command line. Other features of STRADITIZE include text recognition to interpret the names of the different plotted variables, the automatic and semi-automatic recognition of picture artifacts, as well an automatic measurement finder to exactly reproduce the data that has been used to create the diagram. Evaluation of the program has been undertaken comparing the digitization of published figures with the original digital data. This generally shows very good results, although this is inevitably reliant on the quality and resolution of the original figure

Psyplot: Interactive data analysis and visualization with Python

The development, usage and analysis of climate models often requires the visualization of the data. This visualization should ideally be nice looking, simple in application, fast, easy reproducible and flexible. There exist a wide range of software tools to visualize model data which however often lack in their ability of being (easy) scriptable, have low flexibility or simply are far too complex for a quick look into the data. Therefore, we developed the open-source visualization framework psyplot that aims to cover the visualization in the daily work of earth system scientists working with data of the climate system. It is build (mainly) upon the python packages matplotlib, cartopy and xarray and integrates the visualization process into data analysis. This data can either be stored in a NetCDF, GeoTIFF, or any other format that is handled by the xarray package. Due to its interactive nature however, it may also be used with data that is currently processed and not already stored on the hard disk. Visualizations of rastered data on the glob are supported for rectangular grids (following or not following the CF Conventions) or on a triangular grid (following the CF Conventions (like the earth system model ICON) or the unstructured grid conventions (UGRID)). Furthermore, the package visualizes scalar and vector fields, enables to easily manage and format multiple plots at the same time. Psyplot can either be used with only a few lines of code from the command line in an interactive python session, via python scripts or from through a graphical user interface (GUI). Finally, the framework developed in this package enables a very flexible configuration, an easy integration into other scripts using matplotlib

Computing climate-smart urban land use with the Integrated Urban Complexity model (IUCm 1.0)

Cities are fundamental to climate change mitigation, and although there is increasing understanding about the relationship between emissions and urban form, this relationship has not been used to provide planning advice for urban land use so far. Here we present the Integrated Urban Complexity model (IUCm 1.0) that computes “climate-smart urban forms”, which are able to cut emissions related to energy consumption from urban mobility in half. Furthermore, we show the complex features that go beyond the normal debates about urban sprawl vs. compactness. Our results show how to reinforce fractal hierarchies and population density clusters within climate risk constraints to significantly decrease the energy consumption of urban mobility. The new model that we present aims to produce new advice about how cities can combat climate change.</p

A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

While a wide range of Earth system processes occur at daily and even subdaily timescales, many global vegetation and other terrestrial dynamics models historically used monthly meteorological forcing both to reduce computational demand and because global datasets were lacking. Recently, dynamic land surface modeling has moved towards resolving daily and subdaily processes, and global datasets containing daily and subdaily meteorology have become available. These meteorological datasets, however, cover only the instrumental era of the last approximately 120 years at best, are subject to considerable uncertainty, and represent extremely large data files with associated computational costs of data input/output and file transfer. For periods before the recent past or in the future, global meteorological forcing can be provided by climate model output, but the quality of these data at high temporal resolution is low, particularly for daily precipitation frequency and amount. Here, we present GWGEN, a globally applicable statistical weather generator for the temporal downscaling of monthly climatology to daily meteorology. Our weather generator is parameterized using a global meteorological database and simulates daily values of five common variables: minimum and maximum temperature, precipitation, cloud cover, and wind speed. GWGEN is lightweight, modular, and requires a minimal set of monthly mean variables as input. The weather generator may be used in a range of applications, for example, in global vegetation, crop, soil erosion, or hydrological models. While GWGEN does not currently perform spatially autocorrelated multi-point downscaling of daily weather, this additional functionality could be implemented in future versions

Chilipp/psyplot-conda: v1.0.1: Conda installers for psyplot - The interactive visualization framework

psyplot-conda provides standalone installers for the psyplot interactive visualization framework. psyplot is an open source python project that mainly combines the plotting utilities of matplotlib (Hunter, 2007) and the data management of the xarray package (Hoyer &amp; Hamman, 2017). 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. These installers contains all necessary dependencies for psyplot, psyplot-gui, psy-simple, psy-maps and psy-reg plus the conda package for managing virtual environments. The installers have been created using using the conda constructor package and the packages from the conda-forge channel. Other ways to install psyplot can be found in the psyplot docs: http://psyplot.readthedocs.i