Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/MaxStrange/expyplot

Matplotlib for Elixir
https://github.com/MaxStrange/expyplot

elixir elixir-lang matplotlib plotting python

Last synced: 8 days ago
JSON representation

Matplotlib for Elixir

Awesome Lists containing this project

README 

        
# Expyplot



Inspired by this project:

https://github.com/JordiPolo/explot, but I wanted something more transparent.



Documentation can be found at [https://hexdocs.pm/expyplot](https://hexdocs.pm/expyplot).



Expyplot allows you to use 'all' of the functions in matplotlib.pyplot (in reality, there are some that I left out because they don't make any sense in this context, or

are deprecated).



Unfortunately, I have not tested anywhere near all of the functions, but they should mostly work. If any don't work, please open an issue, or better yet,

make a fix and open a pull request! This library is simple enough that you should be able to grok it without too much effort.



Perhaps the most limiting thing about this library is that it currently has to convert return values from the matplotlib functions into strings before

sending them back to Elixir, so rather than returning `true`, you will get `"True"`, and good luck piecing back to gether complicated objects. The reason I went this route

is that JSON is unable to parse pretty much any complicated data type that comes back from a matplotlib function, and I didn't feel like writing a JSON parser. If you

want to do something about it, please by all means, write a JSON parser capable of serializing all the different return types from matplotlib functions: the python

side of this library is very simple - it is located in priv/mat.py.



## Differences



Unfortunately (or rather, fortunately if we are celebrating linguistic diversity!), I could not attain complete transparency. This is a list of some notable differences

between this library and the real matplotlib.pyplot:



- Return values are strings currently

- Variable arguments must be wrapped in a list, so: `plt.plot(a, b)` becomes `Plot.plot([a, b])`, but especially notice: `Plot.plot([[1..10], other_args])`

- Named args and keyword args are atoms: `Plot.grid(b: true)`, though they don't have to be, so: `Plot.plot([[1..10], "r+"])`

- Keyword args that are not keyword only arguments must be written with keywords, you can't do them positionally, so while `plt.grid(True)` works in Python,

  you have to name the argument in Elixir: `Plot.grid(b: true)`; if you ever get the error: "argument error: :erlang.++(something, [])", this is the reason. You need

  to rewrite the argument list as a keyword list.

- Any keyword argument or named argument that starts with a capital letter needs to be renamed with an underscore: `Fs` -> `:_Fs`



## Installation



You must have python3 installed and in your path



You must also pip3 install PyQt5 and matplotlib:

`pip3 install PyQt5`, `pip3 install matplotlib`, or however you install packages for python3 on your particular system.



```elixir

def deps do

  [{:expyplot, "~> 1.1.2"}]

end

```



## Examples



Examples taken from http://matplotlib.org/examples/pylab_examples and then rewritten in Elixir to use this Expyplot.



To run the examples, start an iex -S mix session and run:



```elixir

Code.load_file "./path/to/example/EXAMPLE.exs"

```



### Histogram



```elixir

defmodule HistogramExample do

  alias Expyplot.Plot



  {mu, sigma} = {100, 15}

  x = 1..10_000 |> Stream.map(fn(_) -> Statistics.Distributions.Normal.rand(mu, sigma) end) |> Enum.to_list



  Plot.hist(x, bins: 50, normed: 1, facecolor: :green, alpha: 0.75)

  Plot.xlabel("Smarts")

  Plot.ylabel("Probability")

  Plot.title("IQ Scores")

  Plot.axis_set([40, 160, 0, 0.03])

  Plot.grid(b: true)



  Plot.show()

end

```

![Histogram](images/histogram.png)



### Subplots



```elixir

defmodule SpectralExample do

  alias Expyplot.Plot



  dt = 0.01

  fs = 1 / dt

  t = Stream.unfold(0, fn(acc) -> {acc, acc + dt} end) |> Stream.take_while(&(&1 < 10)) |> Enum.to_list

  nse = t |> Enum.map(fn(_) -> Statistics.Distributions.Normal.rand() end) |> Enum.to_list

  r = t |> Enum.map(&(Statistics.Math.exp(- &1 / 0.05)))



  cnse = nse |> Enum.map(&(&1 * Enum.random(r))) |> Enum.take(length(t)) # fake convolution. I didn't feel like writing a functional convolution.

  s = t |> Enum.map(&(:math.sin(2 * Statistics.Math.pi * &1))) |> Enum.zip(cnse) |> Enum.map(fn {el1, el2} -> el1 + el2 end)



  Plot.subplot([3, 2, 1])

  Plot.plot([t, s])



  Plot.subplot([3, 2, 1])

  Plot.plot([t, s])



  Plot.subplot([3, 2, 3])

  Plot.magnitude_spectrum(s, _Fs: fs)



  Plot.subplot([3, 2, 4])

  Plot.magnitude_spectrum(s, _Fs: fs, scale: :dB)



  Plot.subplot([3, 2, 5])

  Plot.angle_spectrum(s, _Fs: fs)



  Plot.subplot([3, 2, 6])

  Plot.phase_spectrum(s, _Fs: fs)



  Plot.show()

end

```

![Subplots](images/subplots.png)



### Zorder/Scatterplots



```elixir

defmodule ZorderExample do

  alias Expyplot.Plot



  x = 1..20 |> Enum.map(fn(_) -> Statistics.Distributions.Normal.rand() end) |> Enum.to_list

  y = 1..20 |> Enum.map(fn(_) -> Statistics.Distributions.Normal.rand() end) |> Enum.to_list



  Plot.figure()

  Plot.subplot([2, 1, 1])

  Plot.plot([x, y, :r], [lw: 3])

  Plot.scatter(x, y, [s: 120])

  Plot.title("Lines on top of dots")



  Plot.subplot([2, 1, 2])

  Plot.plot([x, y, :r], [zorder: 1, lw: 3])

  Plot.scatter(x, y, [s: 120], [zorder: 2])

  Plot.title("Dots on top of lines")



  Plot.show()

end

```

![Zorder](images/zorder.png)



### Bar Graph



```elixir

defmodule BarExample do

  alias Expyplot.Plot



  men_means = [20, 35, 30, 35, 27]

  women_means = [25, 32, 34, 20, 25]

  men_std = [2, 3, 4, 1, 2]

  women_std = [3, 5, 2, 3, 3]

  ind = 0..4

  width = 0.35

  yticks = Stream.unfold(0, fn(acc) -> {acc, acc + 10} end) |> Stream.take_while(&(&1 < 81)) |> Enum.to_list



  Plot.bar(ind, men_means, [width: width], color: "#d62728", yerr: men_std, label: "Men")

  Plot.bar(ind, women_means, [width: width, bottom: men_means], yerr: women_std, label: "Women")

  Plot.ylabel("Scores")

  Plot.title("Scores by group and gender")

  Plot.xticks([ind, {"G1", "G2", "G3", "G4", "G5"}])

  Plot.yticks([yticks])

  Plot.legend()

  Plot.show()

end

```

![Bar Graph](images/bar.png)