A Julia interface to the plot.ly API

Forked from astrieanna/Plotly.jl

README quickly to get started. Alternately, checkout out the pretty Julia docs at http://plot.ly/api

Installation

Given that you have Julia v0.2.1,

Pkg.clone("https://github.com/plotly/Plotly.jl")

Usage

julia> using Plotly
INFO: Cloning Plotly from https://github.com/plotly/Plotly.jl
INFO: Computing changes...
INFO: No packages to install, update or remove.

You'll need to create a plot.ly account and find out your API key before you'll be able to use this package.

New user signup

julia> Plotly.signup("username","email")
Success! Check your email to activate account.

Signin

julia> Plotly.signin("username","your api key")
PlotlyAccount("username","your api key")

Plot && Open in browser

julia> Plotly.openurl(Plotly.plot(["z"=>rand(6,6)],["style"=>["type"=>"heatmap"]]))
START /bin/firefox "https://plot.ly/~astrieanna/0"

That last line is what the REPL prints out, as a Firefox tab opens with the plot. You can also just call plot by itself, and you'll get a String that's the url of your chart.

Style and Layout

julia> Plotly.style(["line"=>["color"=>"rgb(255,0,0)","width"=>10]])
julia> Plotly.layout(["layout"=>["title"=>"Time Wasted"]])

Quick Plotting

Functions and Polynomials

julia> Plotly.plot(abs)
julia> Plotly.plot([sqrt, log], ["left"=>10, "right"=>20, "step"=>0.1])
julia> Plotly.plot() do x
       savings = 3000
       income = x*1000
       expenses = x*800
       return savings+income-expenses
       end

You can now plot functions directly. The first line shows how to plot the absolute value function, and the second line plots the square root and logarithm functions, both from 10 to 20 at increments of 0.1. The last line shows how to use Julia's do syntax to plot complicated anonymous functions.

julia> using Polynomial
julia> x = Poly([1,0])
julia> Plotly.plot(3x^3 + 2x^2 - x + 1)
julia> Plotly.plot([x, 2x, 3x^2-x])

Using the Polynomial package, you can plot polynomials directly the same way as math functions.

DataFrames and TimeSeries

julia> using DataFrames
julia> df = readtable("height_vs_weight.csv")
julia> Plotly.plot(df, ["xs"=>:height, "ys"=>:weight])

Using the DataFrames package, you can read CSV data and plot it directly by passing the data frame and setting the xs and/or ys options. These are symbols or arrays of symbols refering to columns names in the CSV file.

julia> using TimeSeries
julia> d = [date(2012,5,29):date(2013,5,29)]
julia> t = TimeArray(d, rand(length(d),2), ["foo","bar"])
julia> Plotly.plot(t)

Using the TimeSeries package, you can plot them directly by passing a TimeArray argument.

WAV Files

julia> using WAV
julia> Plotly.plot(wavread("filename.wav"))

Using the WAV package, you can plot WAV files by passing a call to the wavread function.

Detailed Plotting

Arrays and Dicts

julia> trace1 = Plotly.line([3x for x in 1:1000])
julia> trace2 = Plotly.histogram([3x for x in 1:1000])
julia> trace3 = Plotly.scatter([2x => 3x for x in 1:1000])
julia> trace4 = Plotly.box([2x => 3x for x in 1:1000])
julia

Functions and Polynomials

julia> trace1 = Plotly.line(abs, ["left"=>10, "right"=>20, "step"=>0.1])
julia> trace2 = Plotly.box(sin, ["left"=>10, "right"=>20, "step"=>0.1])
julia> trace3 = Plotly.scatter(cos, ["left"=>10, "right"=>20, "step"=>0.1])
julia> trace4 = Plotly.histogram(cos, ["left"=>10, "right"=>20, "step"=>0.1])
julia> Plotly.plot([trace1, trace2, trace3, trace4])

julia> using Polynomial
julia> x = Poly([1,0])
julia> trace1 = Plotly.line(3x^3 + 2x^2 - x + 1)
julia> trace2 = Plotly.histogram(3x^3 + 2x^2 - x + 1)
julia> Plotly.plot([trace1, trace2])

DataFrames and TimeSeries

julia> using DataFrames
julia> df = readtable("height_vs_weight.csv")
julia> trace1 = Plotly.line(df, ["xs"=>:height, "ys"=>:weight])
julia> trace2 = Plotly.scatter(df, ["xs"=>:height, "ys"=>:weight])
julia> trace3 = Plotly.histogram(df, ["xs"=>:height])
julia> trace4 = Plotly.box(df, ["ys"=>:weight])
julia> Plotly.plot([trace1, trace2, trace3, trace4])

julia> using TimeSeries
julia> d = [date(2012,5,29):date(2013,5,29)]
julia> t = TimeArray(d, rand(length(d),2), ["foo","bar"])
julia> trace1 = Plotly.line(t)
julia> trace2 = Plotly.scatter(t)
julia> trace3 = Plotly.box(t)
julia> trace4 = Plotly.histogram(t)
julia> Plotly.plot([trace1, trace2, trace3, trace4])

WAV Files

julia> using WAV
julia> trace1 = Plotly.line(wavread("filename.wav"))
julia> trace2 = Plotly.histogram(wavread("filename.wav"))
julia> trace3 = Plotly.box(wavread("filename.wav"))
julia> trace4 = Plotly.scatter(wavread("filename.wav"))
julia> Plotly.plot([trace1, trace2, trace3, trace4])