PLOTCON NYC: PlotlyJS.jl: Interactive plotting in Julia

: Interactive plotting in Julia
Spencer Lyon
November 18, 2016
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In [1]:
PlotlyJS.jl
http://spencerlyon.com/presentations/
using PlotlyJS
Plotly javascript loaded.
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init_notebook(true)

Outline
Thanks
Bio
Brief intro to Julia
: plotly.js in Julia
Basics
Interactivity
Convenience API
Styles
PlotlyJS.jl

Bio
4th year of economics PhD program at NYU Stern
Live in NYC with wife and three kids
Active in Julia community, occasional sightings in SciPy/PyData world
Research interests are (in broad terms) information and beliefs, international
macro, reinforcement learning -- all with a computational slant

What is Julia?
For me:
Fast enough (in terms of my time and run time) to do research code
Fun to play with!
Julia is a high-level, high-performance dynamic programming
language for technical computing, with syntax that is familiar to
users of other technical computing environments.

Core concept: Multiple dispatch
Julia's core abstraction is multiple dispatch
Functions are specialized based on the type of all arguments
When I call f(x, y), which f is run?
Best understood by example

In [2]: f(x, y) = "Two arguments: $(x) and $(y)"
f("hello", "plotcon")
Out[2]: "Two arguments: hello and plotcon"

In [3]:
In [4]:
In [5]:
In [6]:
f(x::Number, y) = "First arg is a number ($(x)), second isn't ($(y))"
# still old method
f("hello", "plotcon")
# new method
f(2, "plotcon")
# Generics
# also new method, but this time with floating point first argument
f(2.0, "plotcon")
Out[3]: f (generic function with 2 methods)
Out[4]: "Two arguments: hello and plotcon"
Out[5]: "First arg is a number (2), second isn't (plotcon)"
Out[6]: "First arg is a number (2.0), second isn't (plotcon)"

In [7]:
In [8]:
In [9]:
# longer function syntax
function f(x::Number, y::Number)
"Two numbers: ($(x), $(y))"
end
# newest method
f(2.0, 2)
# unsigned 8 bit int and BigInt
f(0x81, big(4))
Out[7]: f (generic function with 3 methods)
Out[8]: "Two numbers: (2.0, 2)"
Out[9]: "Two numbers: (129, 4)"

What's the point?
Our f function isn't useful
Multiple dispatch enables flexibility and expressivity
We'll leverage this in PlotlyJS.jl to make the API convenient

PlotlyJS.jl
Julia wrapper for plotly.js:
Creates plotly plots
Exposes plotly.js API functions to Julia
Two main goals:
1. Make it convenient to construct and manipulate plotly visualizations
from Julia
2. Provide infrastructure for viewing plots on multiple frontends and
saving publication quality plotly graphics to files

API Overview
The PlotlyJS.jl API has two main layers:
1. Faithful plotly.js layer: makes it possible to do anything plotly.js can
2. Convenience, "Julian" layer: WIP attempt to make some things more natural in
Julia

Faithful plotly.js api layer
plotly.js visualzations are described as a JSON object:
Let's look at how we can build these traces and a Layout in Julia
{
// Overall chart attributes
"layout": {
"title": "x²"
},
"data": [
// JSON array of 'traces'
{
// Example trace
"type": "scatter",
"y": [1, 4.0, 9.0],
"marker": {
"symbol": "square"
}
}
]
}

By Hand
JSON is naturally represented as Julia Dict
Example from above:
Building nested Dicts in Julia isn't convenient
Need to spell out Dict
Need to add lots of quotes (similar to raw json)
Also need => to separate keys/values
Dict(
"layout" => Dict(
"title" => "x²"
),
"data" => [
Dict(
"type" => "scatter",
"y" => [1, 4.0, 9.0],
"marker" => Dict(
"symbol" => "square"
)
)
]
)

With PlotlyJS.jl
PlotlyJS.jl eases this burden for Julia programmers
In [10]: plot(scatter(y=[1.0, 4.0, 9.0], marker_symbol="square"),
Layout(title="x²"))
Out[10]:
0 0.5 1 1.5 2
1
2
3
4
5
6
7
8
9
x²

Traces
Let's take a closer look at building trace
Construct traces using the trace type as a function and setting keyword
arguments
Example trace from above:
In [11]:
Notice the syntax marker_symbol.
This sets a nested json atrribute {"marker": {"symbol": "square"}}
See the json
In [12]:
my_trace = scatter(y=[1.0, 4.0, 9.0], marker_symbol="square")
print(json(my_trace, 2))
Out[11]: scatter with fields marker, type, and y
{
"y": [
1.0,
4.0,
9.0
],
"type": "scatter",
"marker": {
"symbol": "square"
}
}

More Examples
In [13]:
In [14]:
# nesting works at more than one level
trace1 = scatter(y=rand(10), marker_color="red",
marker_line_width=2.0)
trace2 = contour(x=1:5, y=1:10, z=randn(5, 10))
trace3 = bar(x=1:10, y=rand(1:10, 10), name="mybar")
print(json(trace1, 2))
Out[13]: bar with fields name, type, x, and y
{
"y": [
0.8168546843804745,
0.1967457511182984,
0.5148182283741638,
0.31298671217983753,
0.10559144576552915,
0.6834355544280886,
0.7503355447535949,
0.33011457537607636,
0.05938681725632655,
0.858053689412962
],
"type": "scatter",
"marker": {
"line": {
"width": 2.0
},
"color": "red"
}
}

All trace types have a corresponding function
All trace attributes are settable. Consult for an overwhelmingly
complete list
api reference

Layout
Build a layout by constructing a Layout object:
In [15]:
The same underscore magic applies
Also notice the attr function. This allows you to create groups of nested
attributes.
foo=attr(x=1, y=2) produces {"foo": {"x": 1, "y": 2}}
It is an alternative to foo_x=1, foo_y=2
attr can be used when building traces also
layout = Layout(xaxis=attr(range=[0, 10],
title="assets"),
yaxis_title="consumption",
title="??")
Out[15]: layout with fields margin, title, xaxis, and yaxis

In [16]: print(json(layout, 2))
{
"yaxis": {
"title": "consumption"
},
"xaxis": {
"range": [
0,
10
],
"title": "assets"
},
"title": "??",
"margin": {
"r": 50,
"l": 50,
"b": 50,
"t": 60
}
}

To actually display a plot, we tie one or more traces together with a layout by calling the
plot function:
In [17]: plot(trace1, layout)
Out[17]:
0 2 4 6 8 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
??
assets
consumption

In [18]: # layout optional
plot(trace2)
Out[18]:

In [19]: # more than one trace
plot([trace1, trace3], layout)
Out[19]:

Convenience API
The plot function has a number of other methods that try to make it a bit easier to
construct simple plots (remember multiple dispatch? :) )
In [20]: methods(plot)
Out[20]: 13 methods for generic function plot:
plot{T<:Number,T2<:Number}(x::AbstractArray{T,1},
y::AbstractArray{T2,2}) at
y::AbstractArray{T2,2}, l::PlotlyJS.Layout; style, kwargs...) at
y::AbstractArray{T2,2}) at
y::AbstractArray{T2,2}, l::PlotlyJS.Layout; style, kwargs...) at
/Users/sglyon/.julia/v0.5/PlotlyJS/src/convenience_api.jl:31

Let's see a few of them in action
In [21]: x = linspace(-5, 5, 50)
y1 = sin(x)
y2 = cos(x)
plot(x, y1)
Out[21]:
−4 −2 0 2 4
−1
−0.5
0
0.5
1

In [22]: # x optional and set attributes with keywords
plot(y1, marker_color="red", mode="markers")
Out[22]:
0 10 20 30 40 50
−1
−0.5
0
0.5
1

In [23]: # columns become traces
plot(x, [y1 y2], kind="bar")
Out[23]:

In [24]: # can pass layout
plot(x, [y1 y2], Layout(title="My sinusoids"))
Out[24]:
−4 −2 0 2 4
−1
−0.5
0
0.5
1
My sinusoids
trace 0
trace 1

In [25]: # can plot functions
plot([sin, _ -> cos(exp(sin(2*_)))], -6, 6, Layout(title="My sinusoids"),
marker_symbol="square", mode="markers+lines", kind="bar")
Out[25]:

Subplots
Declarative APIs for subplots can be verbose
PlotlyJS.jl tries to make this easier for you
Suppose you have creates 4 plots using plot:
You can combine them into subplots using familiar (h|v|hv)cat syntax:
p1 = plot(...)
p2 = plot(...)
p3 = plot(...)
p4 = plot(...)
[p1 p2] # 1 row 2 cols
[p1 p2 p3] # 1 row 3 cols
[p1, p2] # 2 rows 1 col
[p1 p2; p3 p4] # 2 rows 2 cols

Example
In [26]: rand_plot(n) = plot(scatter(x=collect(1:n), y=randn(n)))
p1, p2, p3, p4 = [rand_plot(i) for i in [10, 20, 30, 40]];

In [27]: # 2 columns, 1 row
[p1 p2]
Out[27]:
5 10
−1
−0.5
0
0.5
1
1.5
5 10 15 20
−2
−1.5
−1
−0.5
0
0.5
1
1.5
2
trace 0
trace 1

In [28]: # 3 columns, 1 row
[p1 p2 p3]
Out[28]:
5 10
−1
−0.5
0
0.5
1
1.5
5 10 15 20
−2
−1.5
−1
−0.5
0
0.5
1
1.5
2
10 20 30
−2
−1.5
−1
−0.5
0
0.5
1
trace 0
trace 1
trace 2

In [29]: # two rows
[p1, p2]
Out[29]:
2 4 6 8 10
−1
0
1
5 10 15 20
−2
−1
0
1
2
trace 0
trace 1

In [30]: # two rows and columns
p = [p1 p2; p3 p4]
Out[30]:
5 10
−1
0
1
5 10 15 20
−2
−1
0
1
2
10 20 30
−2
−1
0
1
10 20 30 40
−2
−1
0
1
2
trace 0
trace 1
trace 2
trace 3

... under the hood
We can inspect the json to see what we've been saved from
In [31]: print(json(p))
{"layout":{"xaxis4":{"domain":[0.55,1.0],"anchor":"y4"},"xaxis3":{"domain":[0.0,0.45],"anchor":"y3"},"yaxis2":{"
domain":[0.575,1.0],"anchor":"x2"},"yaxis1":{"domain":[0.575,1.0],"anchor":"x1"},"xaxis1":{"domain":[0.0,0.4
5],"anchor":"y1"},"margin":{"r":50,"l":50,"b":50,"t":60},"xaxis2":{"domain":[0.55,1.0],"anchor":"y2"},"yaxis4":{"
domain":[5.551115123125783e-17,0.42500000000000004],"anchor":"x4"},"yaxis3":{"domain":[5.551115123
125783e-17,0.42500000000000004],"anchor":"x3"}},"data":[{"yaxis":"y1","y":[0.4802673556774336,1.49489
51444362697,-0.07045549774812956,0.7875701715665517,1.19154169230362,-0.019984297282311308
,-1.2188104610632033,1.4889777636767583,0.1937548662633838,0.9467644161632363],"type":"scatter
","xaxis":"x1","x":[1,2,3,4,5,6,7,8,9,10]},{"yaxis":"y2","y":[0.8116890547201712,0.026305587394362537,-1.9
037677467854397,0.5179875294705465,1.132848386254002,-1.1211047904365008,-0.09732503991256
072,-0.797064674081989,-0.5957822490328146,-1.5401700868810337,-0.8094006419764531,1.473379
0412159211,1.8303960585896535,0.5451000885288673,-0.8508167149855682,0.5592069782568302,-0.
5819292461464338,0.08366492698814007,-1.2171127224168015,-1.1731942880984414],"type":"scatter"
,"xaxis":"x2","x":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]},{"yaxis":"y3","y":[0.445644268361460
15,0.43568978674287906,-2.2957964903407775,-1.5588172083692364,-0.5175404777486905,-0.68798
65409420531,-1.3614114948496532,-0.26860396772198347,-0.4550258510176642,-0.66615874133325
51,-0.6568459806232246,-0.655160517941339,-1.0584839635045593,-0.9661978451654369,0.0558816
3077019386,0.4473040023028272,-0.4180878888519696,-0.670930203698033,-1.5680157591455446,-0
.12061123763796747,0.422381416839956,-1.912905533601873,-0.7864305169890853,0.362836708603
15104,1.1179292892139105,0.24868455953112426,0.8826378694420977,0.7852575028904395,-0.7092
479652592288,-0.7193948586827139],"type":"scatter","xaxis":"x3","x":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,
16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]},{"yaxis":"y4","y":[-1.1744007569318526,-0.777333601105
0302,1.4579909852349564,0.645916605678359,0.05423891204909586,-0.5428595063217369,0.788054
5251999888,-0.14699104765892224,-0.3646094249332061,-0.4594704011787981,2.051735462555251,-
0.10082715474940011,-1.1039826364944816,-0.6379899490941968,2.1124562929284965,1.553274330
113308,-0.03180412490346881,0.5406670948650817,0.2738357386022979,-0.15275395057926666,-0.6
982546882702967,-0.30727020052937565,0.214317134550011,-0.5067747133088952,0.0099432180881
08437,-1.3286282576286048,-0.7921663785646528,-1.920398068028969,-0.37936872456340026,0.042
28943262867289,-0.9117834367846034,1.7365583749057234,-0.35923210966846647,-1.097482170333

plotly.js api functions
PlotlyJS.jl also exposes all plotly.js API functions to pure Julia
See list
Let's see some examples
here

In [32]:
In [33]:
In [34]:
In [35]:
In [36]:
myplot = rand_plot(10)
restyle!(myplot, marker_color="ForestGreen")
relayout!(myplot, title="This is my title")
addtraces!(myplot, trace1)
prependtraces!(myplot, 1, x=[[0.1, 0.2, 0.3, 1]], y=[rand(4)])
Out[32]:
2 4 6 8 10
−1.5
−1
−0.5
0
0.5
1
1.5

Interact.jl
We can leverage Interact.jl to put have IPython widgets interact with our plots
In [38]: using Interact

In [39]: x = linspace(-4, 4, 70)
p = plot(x, sin(x), Layout(xaxis_range=(-5, 5),
yaxis_range=(-1.1, 1.1)))
display(p)
colors = ["red", "green", "blue", "orange"]
modes = ["markers", "lines", "markers+lines"]
φs = linspace(-2, 2, 100)
ωs = linspace(-π, π, 100)
@manipulate for c in colors, m in modes, φ in φs, ω in ωs
y = sin.(ω*x + φ)
restyle!(p, y=(y,), marker_color=c, mode=m)
end
−4 −2 0 2 4
−1
−0.5
0
0.5
1

Frontends
The API presented above covers goal 1
Goal 2 includes frontend integration
A key feature of PlotlyJS.jl is integration with IJulia and providing a dedicated
GUI window just for PlotlyJS.jl figures

Electron
We use to provide an app for PlotlyJS.jl
This buys us at least 2 things:
1. Dedicated GUI that we completely control
2. Full 2-way communication with javascript
Javascript interop enables:
Live updates of trace or layout attributes
Extending traces or adding new traces to a displayed plot
Raw svg output from d3.js for conversion to pdf, png, jpeg, eps, etc.
More...
Demo
Blink.jl Electron

IJulia
First class support for jupyter notebooks
Still have interactivity, but communication with javascript goes through a non-
displayed electron window...for now ("native" notebook communication is
close)
Can leverage tools like Interact.jl to tie arbitrary widgets to plot updates

Juno
Can hook into the Juno Plot pane inside Atom
Because Atom is electron, this frontends behaves much like the Electron one

nteract
Reuse integration with Jupyter to render plots in nteract

Styles
PlotlyJS.jl versions >= 0.4.0 have support for plotting styles.
The best way to think about styles is that they will apply default values for
attributes, only if the attribute is not already defined.

Style definitions
Styles are instaces of this type:
immutable Style
color_cycle::Vector
layout::Layout
global_trace::PlotlyAttribute
trace::Dict{Symbol,PlotlyAttribute}
end

Using Syles
There are two main ways to use a Style:
Global mode: call the use_style!(::Style)
Plot by plot mode: All methods of the plot and Plot functions accept a keyword
argument style::Style that sets the style for that plot only.
In [43]: use_style!(:ggplot)
simpleplot(;kw...) = plot([sin, cos], -6, 6; kw...)
simpleplot()
Out[43]:
−0.5
0
0.5
1 sin
cos

In [44]: simpleplot(style=style(:seaborn))
Out[44]:
−6 −4 −2 0 2 4 6
−1
−0.5
0
0.5
1
sin
cos

In [45]: simpleplot(style=style(:fivethirtyeight))
Out[45]:
−6 −4 −2 0 2 4 6
−1
−0.5
0
0.5
1 sin
cos

In [46]: simpleplot(style=style(:tomorrow_night_eighties))
Out[46]:
−6 −4 −2 0 2 4 6
−1
−0.5
0
0.5
1 sin
cos

In [47]:
In [ ]:
simpleplot()
Out[47]:
−6 −4 −2 0 2 4 6
−1
−0.5
0
0.5
1 sin
cos

PLOTCON NYC: PlotlyJS.jl: Interactive plotting in Julia

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