Graphical tools
From Robin
(Difference between revisions)
(→Tips on Exporting Figures) |
|||
| (15 intermediate revisions not shown) | |||
| Line 1: | Line 1: | ||
| + | === Videos === | ||
| + | * For sharing videos with the thesis: figshare | ||
| + | |||
=== Figures/Illustrations === | === Figures/Illustrations === | ||
* Vector Graphics Editors | * Vector Graphics Editors | ||
| Line 6: | Line 9: | ||
** LibreOffice Draw (free and open-source) | ** LibreOffice Draw (free and open-source) | ||
** Through presentation software such as Powerpoint, Keynote, … | ** Through presentation software such as Powerpoint, Keynote, … | ||
| - | ** Web-based options: Google draw (in Google disk), Draw.io, Figma, Lucid charts, … | + | ** Web-based options: Google draw (in Google disk), [https://www.drawio.com Draw.io], Figma, Lucid charts, … |
* Raster Graphics Editors | * Raster Graphics Editors | ||
| Line 22: | Line 25: | ||
** Matplotlib | ** Matplotlib | ||
*** see [https://github.com/jbmouret/matplotlib_for_papers Mouret's tutorial] for publication quality plots | *** see [https://github.com/jbmouret/matplotlib_for_papers Mouret's tutorial] for publication quality plots | ||
| - | *** check the | + | *** check the [https://github.com/matplotlib/cheatsheets cheat sheets] |
** Seaborn (statistical data visualization; uses Matplotlib internally. See for example this [https://elitedatascience.com/python-seaborn-tutorial tutorial]) | ** Seaborn (statistical data visualization; uses Matplotlib internally. See for example this [https://elitedatascience.com/python-seaborn-tutorial tutorial]) | ||
| + | ** [https://github.com/juuf/IN5490/blob/main/IN5490.ipynb Jupyter Notebook] (former ROBIN PhD-student Julian) | ||
* R | * R | ||
** ggplot2 (with Rstudio) | ** ggplot2 (with Rstudio) | ||
| Line 30: | Line 34: | ||
* Gnuplot | * Gnuplot | ||
* Desmos.com (web based) | * Desmos.com (web based) | ||
| - | |||
* excel (UiO) | * excel (UiO) | ||
| Line 44: | Line 47: | ||
** Preferably use vector graphics such as svg, pdf or eps (can be edited with vector graphics editors) | ** Preferably use vector graphics such as svg, pdf or eps (can be edited with vector graphics editors) | ||
** Check your colour maps! (see https://www.nature.com/articles/s41467-020-19160-7 or https://matplotlib.org/stable/tutorials/colors/colormaps.html) | ** Check your colour maps! (see https://www.nature.com/articles/s41467-020-19160-7 or https://matplotlib.org/stable/tutorials/colors/colormaps.html) | ||
| - | ** Check font size, font type, line | + | ** Check font size, font type, line width, marker size, proportion, aspect ratio and resolution! Tuning those parameters makes a significant impact on how your figures are perceived! |
** Example plots with mostly default (left) and adapted (right) plotting parameters. | ** Example plots with mostly default (left) and adapted (right) plotting parameters. | ||
** [[Fil:TestT .png|400px|middle]][[Fil:Test.png|250px|middle]] | ** [[Fil:TestT .png|400px|middle]][[Fil:Test.png|250px|middle]] | ||
| + | ** Make sure that your figures are still readable when printing them in grayscale | ||
* Sometimes it is neccessary to use png format (e.g. in case of render graphics or plots with an essential transparency effect) | * Sometimes it is neccessary to use png format (e.g. in case of render graphics or plots with an essential transparency effect) | ||
** In that case, one should pick a proper resolution for the export file while accounting for its file size | ** In that case, one should pick a proper resolution for the export file while accounting for its file size | ||
| Line 59: | Line 63: | ||
*** R: https://github.com/daqana/tikzDevice | *** R: https://github.com/daqana/tikzDevice | ||
*** Matlab/Octave: https://github.com/matlab2tikz/matlab2tikz | *** Matlab/Octave: https://github.com/matlab2tikz/matlab2tikz | ||
| - | ** It is possible to either input the code directly through <code>\input{}</code> or to compile the figure first [https://blog.modelworks.ch/producing-stand-alone-figures-with-tikz-in-latex/ through] the ''standalone'' class | + | *** It is possible to either input the code directly through <code>\input{}</code> or to compile the figure first [https://blog.modelworks.ch/producing-stand-alone-figures-with-tikz-in-latex/ through] the ''standalone'' class |
| + | * To see how plotting parameters can be tuned given concrete examples, see [https://github.com/juuf/IN5490/blob/5a4a6bb0098402166ceb623bcfd5c3402c5b4f72/IN5490.ipynb here]. Based on an example data set, first distributional plots are shown. Then the default plotting parameters are tuned. | ||
Current revision as of 12:58, 4 April 2024
Contents |
Videos
- For sharing videos with the thesis: figshare
Figures/Illustrations
- Vector Graphics Editors
- Inkscape (free and open-source)
- Adobe Illustrator (for students only through UiO programkiosk)
- Directly in LaTeX (PGF/TikZ: https://github.com/pgf-tikz/pgf; some examples)
- LibreOffice Draw (free and open-source)
- Through presentation software such as Powerpoint, Keynote, …
- Web-based options: Google draw (in Google disk), Draw.io, Figma, Lucid charts, …
- Raster Graphics Editors
- Gimp (free and open-source)
- Adobe Photoshop (for students only through UiO programkiosk)
- Photopea (online editor)
- 3D Computer Graphics Editors
- Blender (free and open-source)
- Autodesk Maya
- see also here
Plotting Apps
- Python
- Matplotlib
- see Mouret's tutorial for publication quality plots
- check the cheat sheets
- Seaborn (statistical data visualization; uses Matplotlib internally. See for example this tutorial)
- Jupyter Notebook (former ROBIN PhD-student Julian)
- Matplotlib
- R
- ggplot2 (with Rstudio)
- Matlab (available at UiO)
- Octave (free alternative to matlab)
- Gnuplot
- Desmos.com (web based)
- excel (UiO)
Digitalize Figures
To qualitatively compare one's own data with other published data, it is sometimes needed to obtain the concrete data of the respective publication. In that case there are multiple ways to do that:
- Python: https://github.com/dilawar/PlotDigitizer
- Matlab: https://blogs.mathworks.com/steve/2013/12/31/automating-data-extraction-1/
- External App: WebPlotDigitizer
Tips on Exporting Figures
- Before saving the graphics in the respective programs
- Avoid bitmap graphics if possible and especially jpg!
- Preferably use vector graphics such as svg, pdf or eps (can be edited with vector graphics editors)
- Check your colour maps! (see https://www.nature.com/articles/s41467-020-19160-7 or https://matplotlib.org/stable/tutorials/colors/colormaps.html)
- Check font size, font type, line width, marker size, proportion, aspect ratio and resolution! Tuning those parameters makes a significant impact on how your figures are perceived!
- Example plots with mostly default (left) and adapted (right) plotting parameters.
- 400px|middle250px|middle
- Make sure that your figures are still readable when printing them in grayscale
- Sometimes it is neccessary to use png format (e.g. in case of render graphics or plots with an essential transparency effect)
- In that case, one should pick a proper resolution for the export file while accounting for its file size
- Possible ways to compress the png file is to use for example pngquant, AdvanceCOMP or ImageMagick
- Exporting to LaTeX
- Recommended approach:
- export as pdf
- optionally use pdfcrop (usually installed with LaTeX) to cut unecessary white space
- in case only eps export is supported, use epstopdf (usually installed with LaTeX)
- For direct inclusion in LaTeX, use the following scripts to create PGF/TikZ files:
- Python: https://github.com/nschloe/tikzplotlib
- R: https://github.com/daqana/tikzDevice
- Matlab/Octave: https://github.com/matlab2tikz/matlab2tikz
- It is possible to either input the code directly through
\input{}or to compile the figure first through the standalone class
- Recommended approach:
- To see how plotting parameters can be tuned given concrete examples, see here. Based on an example data set, first distributional plots are shown. Then the default plotting parameters are tuned.
