![]() If the data file has been updated, you can reload the datasets easily: just right-click on the data filename (inside the Data panel on the right) and select Reload. Now, you can use data_dif like any other dataset, and will automatically update if the original data changes. Add a dataset name (e.g.: data_dif) and click Create. You may need to change the order of the datasets depending on the data. Select Expression using existing datasets and add this expression inside Value, replacing data_1 with the dataset name: Create a dataset with the differences between the records of an existing datasetĬreate an “intermediate” dataset ( Data -> Create. mean() is a Numpy function, check Numpy documentation for more info on available functions. On the properties panel, add this expression on the Function input box: mean(DATA('data_1')) (replace data_1 with the name of the dataset). To add a line to a plot displaying the mean of the data, click on Insert -> Function or on the appropriate toolbar icon (with the description “Plot a function”). Check the page from the previous paragraph for more info. Change “Format” from “Auto” to a custom formatting like this one: %VDm-%VDY (to show the month and the year). In a similar way, you can change the datetime format displayed on the axis ticks. Select the axis, on the “Formatting” panel go to the “Tick labels” tab. Tips Change the number format from scientific to decimal If, after exporting as a PNG, you see a transparent background, click on the page widget (inside Editing panel) and uncheck Hide on the Formatting panel. or on the yellow diskette on the toolbar to export the plot as an bitmap (JPG, PNG,…) or a vector (PDF, SVG,…). To change the format of the axis tick labels (you can see in the previous image that the dates are not displayed properly), select the axis on the “Editing” panel, and change “Mode” to “Datetime”.Ĭlick on File -> Export. Now you can play with the “Fomatting” options to customize your plot. If your data has column headers, you need to change the default value to the column name. The most important for now are “X data” and “Y data”. On the left panel you’ll see the properties of the selected widget. On the toolbar you can see al the plots you can create (points/lines, bars, boxplot,…) and other “widgets” like text labels, plot keys (plot legend), shapes, etc. Now you can see your data columns on the right panel. At the bottom of the dialog, press Import to import the data. For CSV, you can specify if your data file has a column header ( Behaviour -> Header mode), the delimiter ( Delimiters -> Column) or the date format of your data. Then, depending on the file type, you can specify some options. Veusz supports CSV (or any plain text file), HDF5, FITS, QDP and more. Click on Data -> Import or on the blue diskette icon. ![]() First steps Import dataĪfter opening the app, you need to import your data. ![]() Veusz is available on Debian and Fedora repositories (among others) and as a Flatpak. Create a dataset with the differences between the records of an existing dataset. ![]() Change the number format from scientific to decimal.It’s a great program for those who are looking for and easy-to-use program, and don’t want to use the command prompt (although you can use Veusz with the command line if you want). Veusz allows you to create a wide variety of plots using a graphical interface but with all the power of Python and the Numpy library.
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