Open Source Python Data Visualization Software - Page 13

g2 is an easy to use, portable and powerful 2D graphics library. It provides a comprehensive set of functions for simultaneous generation of graphical output on different types of devices.

is a interactive molecular visualisation program designed especially designed for coarse-grained simulation of non-spherical particles. Ellipsoids (Gay-Berne), disks, and custom shaped uniaxial particle (e.g. pear-shaped particles) can be represented.

A Python package for interactive geospatial analysis and visualization with Google Earth Engine. Geemap is a Python package for geospatial analysis and visualization with Google Earth Engine (GEE), which is a cloud computing platform with a multi-petabyte catalog of satellite imagery and geospatial datasets. During the past few years, GEE has become very popular in the geospatial community and it has empowered numerous environmental applications at local, regional, and global scales. GEE provides both JavaScript and Python APIs for making computational requests to the Earth Engine servers. Compared with the comprehensive documentation and interactive IDE (i.e., GEE JavaScript Code Editor) of the GEE JavaScript API, the GEE Python API has relatively little documentation and limited functionality for visualizing results interactively. The geemap Python package was created to fill this gap.

Python based webservice to manage and display information from some fiber-GISes.

A toolbox of open source geospatial essentials

This is a collection of Python functions, with which data can be exported from the Python environment to a file which can be viewed in GoogleEarth.

Geo-Spatial Data Viewer (GSDView) is a lightweight viewer for geo-spatial data and products. It is written in python and Qt4 and uses the GDAL library. GSDView is modular and has a simple plug-in architecture.

Harkay un software desarrollado en Python para la administraccion de un firewall de acceso a internet diseñado teniendo en cuenta su facilidad de uso y potencia. La politica por defecto es denegar todo habilitando puntualmente lo que se desea.

Python project with goal to provide FOSS library to extract, analyse and visualise data in a 3D fashion. The instance will connect to a data source, ods sheet, csv, sql DB, pyodbc the instance will analyse and/or transform the data to be presented to the visualisation functionality the instance will visualise the data in a 3D fashion, likely using third party FOSS

iTag has been designed for researchers that rely on photographic census techniques of animals that are hard to detect via image recognition algorithms and was originally developed for counting Grey Seals in the German wadden sea during March 2013. It has since then been further expanded and has now reached beta status. iTag allows Users to define up to 9 different categories and name them accordingly. In addition, 4 modifiers are available to further increase the options during a tagging session. Users are able to load a series of Images into a session and add tags on objects on these images within previously defined categories and modifiers. Upon ending the session, result files are produced including (if provided by the EXIF data) the gps information for each Picture, the number of objects in each category and a detailed result file that describes each individual object. In addition, all images that were tagged are saved.

This is the public icram.de code repository filled with code/code-snippets and other useful stuff.

Small python script that can visualize Inter Process Communication on Linux. It can create a graph of TCP and Unix sockets, pipes and also process hierarchy. Short (59 sec) demo visualizing ssh client connecting to ssh server on the same Ubuntu host: http://youtu.be/8XFKwzkexQY Longer (2 min 13 sec) demo visualizing ZoneMinder surveillance solution operation (stopping, starting, enabling and viewing camera): http://youtu.be/kM7klE61Ibk Tested on Ubuntu 14.10 and Debian 7. Quick start on Ubuntu/Debian: # apt-get install graphviz python-pygraphviz # ./ipcvis.py

Create charts with Python in a very similar way to creating charts using Chart.js. The charts created are fully configurable, interactive, and modular and are displayed directly in the output of the cells of your jupyter notebook environment. Charts are fully interactive, you can hover it to display tooltips and select the information you want to see directly from the output cell of your notebook. All the types of charts present in Chart.js are exposed in ipychart. Even complex features such as mixed-types charts are available. Charts are highly customizable and all Chart.js options are available in ipychart. You have complete control over the appearance of your chart.

A widget enabling interactive graph visualization with cytoscape.js in JupyterLab and the Jupyter Notebook.

Yet another GUI for Kernel-based Virtual Machines.

A Python package for geospatial analysis and interactive mapping in a Jupyter environment. Leafmap is a Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment. It is a spin-off project of the geemap Python package, which was designed specifically to work with Google Earth Engine (GEE). However, not everyone in the geospatial community has access to the GEE cloud computing platform. Leafmap is designed to fill this gap for non-GEE users. It is a free and open-source Python package that enables users to analyze and visualize geospatial data with minimal coding in a Jupyter environment, such as Google Colab, Jupyter Notebook, and JupyterLab. Leafmap is built upon several open-source packages, such as folium and ipyleaflet (for creating interactive maps), WhiteboxTools and whiteboxgui (for analyzing geospatial data), and ipywidgets (for designing interactive graphical user interface [GUI]).

Python software package for 3D tracking of leg joints in walking insects. Published as Bender et al., PLoS ONE, 2010 (http://dx.doi.org/10.1371/journal.pone.0013617)

L(atex)equation aims to be the simplest latex to bitmap converter there is. Its goals are: simple gui, simple install, mac + windows + ubuntu binaries and it just should work.

TODO

Messy datasets? Missing values? missingno provides a small toolset of flexible and easy-to-use missing data visualizations and utilities that allows you to get a quick visual summary of the completeness (or lack thereof) of your dataset. Just pip install missingno to get started. This quickstart uses a sample of the NYPD Motor Vehicle Collisions Dataset dataset. The msno.matrix nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion. At a glance, date, time, the distribution of injuries, and the contribution factor of the first vehicle appear to be completely populated, while geographic information seems mostly complete, but spottier. The sparkline at right summarizes the general shape of the data completeness and points out the rows with the maximum and minimum nullity in the dataset. This visualization will comfortably accommodate up to 50 labelled variables.

nb-clean cleans Jupyter notebooks of cell execution counts, metadata, outputs, and (optionally) empty cells, preparing them for committing to version control. It provides both a Git filter and pre-commit hook to automatically clean notebooks before they're staged, and can also be used with other version control systems, as a command line tool, and as a Python library. It can determine if a notebook is clean or not, which can be used as a check in your continuous integration pipelines. nb-clean can also be used as a pre-commit hook. You may prefer this to the Git filter if your project already uses the pre-commit framework. Note that the Git filter and pre-commit hook work differently, with different effects on your working directory. The pre-commit hook operates on the notebook on disk, cleaning the copy in your working directory. The Git filter cleans notebooks as they are added to the index, leaving the copy in your working directory dirty.

NCVTK: A VTK-based tool to visualize data stored in the NetCDF file format.

This toolbox generates automatically a vtk-visualization for an ode (Open Dynamics Engine) simulation under Python. You only have to define the simulation space and world and forward these entities to the visualization. That's all... Install also via pip possible: pip install odeViz

openSim is a software platform for the creation, simulation and visualization of system dynamics models.

Real-time data visualization. Graphically view your data as it's being generated. Peakster is a very intuitive GUI written entirely in Python, which monitors data at regular intervals and uses gnuplot to render 2D, 3D and superimposed plots.