# Graphical tools

### From Robin

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- | === | + | === Figures/Illustrations === |

+ | * Vector Graphics Editors | ||

+ | ** Inkscape (free and open-source) | ||

+ | ** Adobe Illustrator (for students only through [http://www.uio.no/tjenester/it/maskin/programvare/programkiosk/ UiO programkiosk]) | ||

+ | ** Directly in LaTeX (PGF/Ti''k''Z: https://github.com/pgf-tikz/pgf; some [https://texample.net/tikz/examples/ 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 [http://www.uio.no/tjenester/it/maskin/programvare/programkiosk/ UiO programkiosk]) |

- | * [http://www.uio.no/tjenester/it/maskin/programvare/programkiosk/ | + | ** Photopea (online editor) |

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- | * Photopea (online editor) | + | |

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- | + | *3D Computer Graphics Editors | |

- | * | + | ** Blender (free and open-source) |

- | ** | + | ** Autodesk Maya |

- | ** | + | ** see also [https://robin.wiki.ifi.uio.no/3D-software here] |

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- | === | + | === Plotting Apps === |

- | * | + | * Python |

- | ** | + | ** Matplotlib |

- | * | + | *** see [https://github.com/jbmouret/matplotlib_for_papers Mouret's tutorial] for publication quality plots |

- | * | + | *** 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]) | ||

+ | * 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: [https://automeris.io/WebPlotDigitizer/ 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. | ||

+ | ** [[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) | ||

+ | ** 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 [https://pngquant.org/ pngquant], [http://www.advancemame.it/comp-readme AdvanceCOMP] or [https://www.smashingmagazine.com/2015/06/efficient-image-resizing-with-imagemagick/ 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/Ti''k''Z 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 <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 09:09, 28 March 2022

## Contents |

### Figures/Illustrations

- Vector Graphics Editors
- Inkscape (free and open-source)
- Adobe Illustrator (for students only through UiO programkiosk)
- Directly in LaTeX (PGF/Ti
*k*Z: 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)

- 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/Ti
*k*Z 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.