Graphical tools

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=== figures / illustrations ===
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=== Figures/Illustrations ===
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* Vector Graphics Editors
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** Inkscape (free and open-source)
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** Adobe Illustrator (for students only through [http://www.uio.no/tjenester/it/maskin/programvare/programkiosk/ UiO programkiosk])
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** Directly in LaTeX (PGF/Ti''k''Z: https://github.com/pgf-tikz/pgf; some [https://texample.net/tikz/examples/ examples])
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** LibreOffice Draw (free and open-source)
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** Through presentation software such as Powerpoint, Keynote, …
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** Web-based options: Google draw (in Google disk), Draw.io, Figma, Lucid charts, …
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* illustrator - for students only in kiosk?
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* Raster Graphics Editors
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* powerpoint
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** Gimp (free and open-source)
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* directly in Latex (Ti''k''Z/PGF: https://github.com/pgf-tikz/pgf)
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** Adobe Photoshop (for students only through [http://www.uio.no/tjenester/it/maskin/programvare/programkiosk/ UiO programkiosk])
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* [http://www.uio.no/tjenester/it/maskin/programvare/programkiosk/ uio programkiosk]
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** Photopea (online editor)
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* inkscape
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* lucid charts
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* Google draw, in Google disk
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* Photopea (online editor)
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* figma?
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* draw.io
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* linux-program for grafer, finn ut navn
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=== plotting, graphs ===
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*3D Computer Graphics Editors
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* '''matplotlib''' (python, free)
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** Blender (free and open-source)
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** [https://github.com/jbmouret/matplotlib_for_papers Mouret's tutorial for publication quality plots ]
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** Autodesk Maya
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** seaborn - easy to create good looking plots ([https://elitedatascience.com/python-seaborn-tutorial tutorial here])
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** see also [https://robin.wiki.ifi.uio.no/3D-software here]
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** export to Latex (Ti''k''Z/PGF; https://pypi.org/project/tikzplotlib/)
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* '''R - ggplot2 (with Rstudio)''' (free)
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* gnuplot (free)
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* desmos.com (web based)
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* matlab (available at uio)
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** export to Latex (Ti''k''Z/PGF; https://github.com/matlab2tikz/matlab2tikz)
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* Octave (free alternative to matlab)
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* javaFX
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* excel (uio)
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=== How to convert to latex format ===
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=== Plotting Apps ===
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* recommended approach: save as pdf, use pdfcrop, includegraphics in latex
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* Python
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** avoid bitmap graphics if possible and especially jpg!
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** Matplotlib
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* epstopdf - usually installed with latex
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*** see [https://github.com/jbmouret/matplotlib_for_papers Mouret's tutorial] for publication quality plots
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* pdfcrop - usually installed with latex
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*** check the [https://github.com/matplotlib/cheatsheets cheat sheets]
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** Seaborn (statistical data visualization; uses Matplotlib internally. See for example this [https://elitedatascience.com/python-seaborn-tutorial tutorial])
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* R
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** ggplot2 (with Rstudio)
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* Matlab (available at UiO)
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* Octave (free alternative to matlab)
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* Gnuplot
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* Desmos.com (web based)
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* excel (UiO)
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=== Digitalize Figures ===
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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:
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* Python: https://github.com/dilawar/PlotDigitizer
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* Matlab: https://blogs.mathworks.com/steve/2013/12/31/automating-data-extraction-1/
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* External App: [https://automeris.io/WebPlotDigitizer/ WebPlotDigitizer]
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add more info here
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=== Tips on Exporting Figures  ===
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* Before saving the graphics in the respective programs
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** Avoid bitmap graphics if possible and especially jpg!
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** Preferably use vector graphics such as svg, pdf or eps (can be edited with vector graphics editors)
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** Check your colour maps! (see https://www.nature.com/articles/s41467-020-19160-7 or https://matplotlib.org/stable/tutorials/colors/colormaps.html)
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** 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!
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** Example plots with mostly default (left) and adapted (right) plotting parameters.
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** [[Fil:TestT .png|400px|middle]][[Fil:Test.png|250px|middle]]
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** Make sure that your figures are still readable when printing them in grayscale
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* Sometimes it is neccessary to use png format (e.g. in case of render graphics or plots with an essential transparency effect)
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** 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]
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* Exporting to LaTeX
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** Recommended approach:
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*** export as pdf
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*** optionally use pdfcrop (usually installed with LaTeX) to cut unecessary white space
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*** in case only eps export is supported, use epstopdf (usually installed with LaTeX)
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** For direct inclusion in LaTeX, use the following scripts to create PGF/Ti''k''Z files:
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*** Python: https://github.com/nschloe/tikzplotlib
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*** R: https://github.com/daqana/tikzDevice
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*** Matlab/Octave: https://github.com/matlab2tikz/matlab2tikz
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*** 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
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* 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.

Nåværende revisjon fra 28. mar 2022 kl. 09:09

Innhold

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
    • Seaborn (statistical data visualization; uses Matplotlib internally. See for example this 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:

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.
    • 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
  • 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.
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