If changes are made within the original file, this will change your Veusz file also. One thing to keep in mind is that Veusz will link to the original dataset file. If you collect your data in Microsoft Excel or another program, be sure to export the data into one of these allowable file formats. Veusz can import data from the following formats: txt, csv, fits, 2D, or other formats (if you write a plug-in to allow this). Often it may be more convenient to collect data in a program other than Veusz, such as Microsoft Excel. This opens up an interactive Veusz window that will take you step-by-step through the program features. Click the "help" button and click on the "tutorial" button. Veusz also has a great built-in interactive tutorial. This video introduces you to the user interface and shows you how to create a simple plot in Veusz. Tutorials and Resources Ī great introductory video provides you with a simple tour around Veusz. This section will grow and develop as we become more familiar with Veusz. Learning curve to becoming familiar with the user interface.Veusz is not capable of producing 3-D plots.This provides you with more freedom in data manipulation and expression. More formatting features and options compared to Microsoft Excel.This is nice because you can't easily do this with Microsoft Excel and some journals require these file formats. Veusz can export files as EPS, PDF, SVG, or bitmap formats directly.It's ideally suited to handling and manipulating scientific data. There are also distribution packages for Debian, Fedora, FreeBSD, Gentoo, and more. Veusz can be downloaded onto a variety of platforms including Linux, Windows, and Mac OS X. Examples of the types of plots that can be prepared using Veusz can be found here. This software prepares professional looking plots that can be readily submitted to scientific journals. Veusz, pronounced "views", is a free and open source scientific plotting software that was written in Python. This page describes how to use Veusz to create scientific plots. 1 Using Veusz to Create Scientific Plots.It may take some more time at the beginning, but with the advantage that you will not have to repeat the process when, for some reason, you need to modify the plots you generated with matplotlib (to add more data or modify the parameters of your analysis, just to name a few reasons). The basic operations are similar to what you would do in a vector graphics editor, but instead of using a mouse you will do some scripting (I am sure you love it as much as I do). It is written completely in Python and uses only standard libraries. To this end, I will use a small Python package I wrote with this purpose svgutils. Now, we would like to combine both plots into one figure and add some annotations (such as one-letter labels: A,B, etc.). You may try to open one of them in a text editor to find out what I mean. Not going too much into details, I will only say that SVG files are text files with special predefined tags (much alike HTML tags). You may download the scripts I will use in the example from github repository: anscombe.py and sigmoid_fit.py.Ī nice feature of matplotlib is that it allows to export figure to Scalable Vector Graphics (SVG) which is an open vector format understood by many applications (such as Inkscape, Adobe Illustrator or even web browsers). Here, I will describe an automatic workflow which completely resides on Python tools.įirst you need to create nice matplotlib-based plots you would like to compose your figure from. Therefore it makes sense to try and automate the process. This includes manual editing and arranging the figure, which is obviously time consuming. To make things worse you may need to repeat the process several times, when, for example, you want to include more data into the analysis.
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