https://github.com/briochemc/juliaproplotexamples
The proplot examples translated to Julia using PyCall.jl and PyPlot.jl.
https://github.com/briochemc/juliaproplotexamples
cartopy examples julia julialang maps matplotlib plots plotting proplot publication publication-quality-plots pycall pyplot python tutorials
Last synced: 3 months ago
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The proplot examples translated to Julia using PyCall.jl and PyPlot.jl.
- Host: GitHub
- URL: https://github.com/briochemc/juliaproplotexamples
- Owner: briochemc
- License: mit
- Created: 2020-04-24T06:18:37.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-05-07T07:14:10.000Z (about 5 years ago)
- Last Synced: 2024-10-11T10:29:57.320Z (7 months ago)
- Topics: cartopy, examples, julia, julialang, maps, matplotlib, plots, plotting, proplot, publication, publication-quality-plots, pycall, pyplot, python, tutorials
- Language: Julia
- Size: 1.33 MB
- Stars: 9
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Julia ProPlot Examples
Here you will find some (most?) of the [ProPlot](https://proplot.readthedocs.io/en/latest/index.html) examples from the documentation, but translated to Julia using [PyCall.jl](https://github.com/JuliaPy/PyCall.jl).
Note you must install ProPlot first. (And probably some other things.)
* In order to get the colorbars to position/work properly, you need `xarray`. However, note that this causes glitches in LaTex formmatting (i.e. `xlabel=L"$\tau$"` doesn't work anymore.
* In order to address the above, you need to install and use the Julia Package `LaTeXStrings.jl`. This package is inbuilt into `PyPlot.jl` and reexported for use, however here you must call it explicitly via `using LaTeXStrings`
---
Click on the images below to be directed to the julia code that produced it.
## Basics
### Figures and subplots
[
](src/basics/Figures-and-subplots.jl)
### Formatting subplots
[
](src/basics/Formatting-subplots.jl)
### Changing rc settings
[
](src/basics/Changing-rc-settings.jl)
### Subplot grids
[
](src/basics/Subplot-grids.jl)
## Subplots features
### Automatic figure sizing
[
](src/subplots/Automatic-figure-sizing-1.jl)
[
](src/subplots/Automatic-figure-sizing-2.jl)
[
](src/subplots/Automatic-figure-sizing-3.jl)
### Automatic subplot spacing
[
](src/subplots/Automatic-subplot-spacing-1.jl)
[
](src/subplots/Automatic-subplot-spacing-2.jl)
### Arbitrary physical units
[
](src/subplots/Arbitrary-physical-units.jl)
### Subplot numbers and a-b-c labels
[
](src/subplots/Subplot-numbers-and-a-b-c-labels.jl)
### Shared and spanning labels
[
](src/subplots/Shared-and-spanning-labels-1.jl)
[
](src/subplots/Shared-and-spanning-labels-2.jl)
## Colorbars and legends
### Axes colorbars and legends
[
](src/colorbars_legends/Axes-colorbars-and-legends-1.jl)
[
](src/colorbars_legends/Axes-colorbars-and-legends-2.jl)
## Notes
1. Python's 0-based indexing means all the indices are shifted in these Julia examples.
1. The scoping rules are a bit different between Python and Julia, which require some tweaking for the examples to work.
1. You can't format all the subplots at once with `axs.format` AFAIK, so you have to loop through all axes and format each of them.
1. `f, axs = proplot.subplots()` returns a vector of axes, even if you specify multiple columns, so that you need to reshape (and transpose) the `axs` array to get access to the axes in Cartesian coordinates (i.e., as in `axs[i,j]`).
1. I saved the images via `f.savefig("X.svg", transparent=false)` in [figs/](figs/).