Notebook

$LaTeX$ PLots with
MATLAB, Python, R, and a GUI

Plotly is an online graphing and analytics project. It's like a GitHub for data and graphs. The goal is: collaboratively make beautiful, interactive plots, 3D plots, and streaming graphs. It's all in your browser, for free, and without downloads or software installations.

Supporting scientific plotting and collaboration is key to Plotly's mission. You can use $LaTeX$ from any language and a GUI. Starting with Plotly's matplotlib support we'll make an example. For more, head over to our APIs:

I. $LaTeX$ and matplotlib¶

You can follow along by copy and pasting this code right into your termainal. Before getting started, run $ pip install plotly, and if that didn't work, try $ sudo pip install plotly. I always feel like a boss installing with sudo.

In [1]:

import plotly
plotly.__version__

Out[1]:

'1.0.12'

You can use Plotly's public account and keys or sign-up for free.

In [2]:

import matplotlib.pyplot as plt   # side-stepping mpl's backend
import plotly.plotly as py
import plotly.tools as tls
from plotly.graph_objs import *
%matplotlib inline
py.sign_in("IPython.Demo", "1fw3zw2o13")

Here is our first matplotlib figure.

In [3]:

fig1 = plt.figure()

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-2.0, 2.0, 10000) # The x-values
sigma = np.linspace(0.4, 1.0, 4) # Some different values of sigma

# Here we evaluate a Gaussians for each sigma
gaussians = [(2*np.pi*s**2)**-0.5 * np.exp(-0.5*x**2/s**2) for s in sigma]

ax = plt.axes()

for s,y in zip(sigma, gaussians):
    ax.plot(x, y, lw=1.25, label=r"$\sigma = %3.2f$"%s)

formula = r"$y(x)=\frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{x^2}{2\sigma^2}}$"

ax.text(0.05, 0.80, formula, transform=ax.transAxes, fontsize=20)
ax.set_xlabel(r"$x$", fontsize=18)
ax.set_ylabel(r"$y(x)$", fontsize=18)
ax.legend()
plt.show()

To convert it to Plotly, I call Plotly (check out our NB on matplotlib support for more details).

In [4]:

py.iplot_mpl(fig1) 

Now if I want I can print the figure or get the data. I can also call in-line help from Plotly.

In [5]:

from plotly.graph_objs import Data, Layout, Figure

In [6]:

help(Figure)

Help on class Figure in module plotly.graph_objs.graph_objs:

class Figure(PlotlyDict)
 |  A dictionary-like object representing a figure to be rendered in plotly.
 |  
 |      This is the container for all things to be rendered in a figure.
 |  
 |      For help with setting up subplots, run:
 |      `help(plotly.tools.get_subplots)`
 |  
 |      
 |  Quick method reference:
 |  
 |      Figure.update(changes)
 |      Figure.strip_style()
 |      Figure.get_data()
 |      Figure.to_graph_objs()
 |      Figure.validate()
 |      Figure.to_string()
 |      Figure.force_clean()
 |  
 |  Valid keys:
 |  
 |      data [required=False] (value=Data object | dictionary-like):
 |          A list-like array of the data that is to be visualized.
 |  
 |          For more, run `help(plotly.graph_objs.Data)`
 |  
 |      layout [required=False] (value=Layout object | dictionary-like):
 |          The layout dictionary-like object contains axes information, gobal
 |          settings, and layout information related to the rendering of the
 |          figure.
 |  
 |          For more, run `help(plotly.graph_objs.Layout)`
 |  
 |  Method resolution order:
 |      Figure
 |      PlotlyDict
 |      __builtin__.dict
 |      __builtin__.object
 |  
 |  Methods defined here:
 |  
 |  __init__(self, *args, **kwargs)
 |  
 |  ----------------------------------------------------------------------
 |  Methods inherited from PlotlyDict:
 |  
 |  force_clean(self)
 |      Attempts to convert to graph_objs and call force_clean() on values.
 |      
 |      Calling force_clean() on a PlotlyDict will ensure that the object is
 |      valid and may be sent to plotly. This process will also remove any
 |      entries that end up with a length == 0.
 |      
 |      Careful! This will delete any invalid entries *silently*.
 |  
 |  get_data(self)
 |      Returns the JSON for the plot with non-data elements stripped.
 |  
 |  strip_style(self)
 |      Strip style from the current representation.
 |      
 |      All PlotlyDicts and PlotlyLists are guaranteed to survive the
 |      stripping process, though they made be left empty. This is allowable.
 |      
 |      Keys that will be stripped in this process are tagged with
 |      `'type': 'style'` in the INFO dictionary listed in graph_objs_meta.py.
 |      
 |      This process first attempts to convert nested collections from dicts
 |      or lists to subclasses of PlotlyList/PlotlyDict. This process forces
 |      a validation, which may throw exceptions.
 |      
 |      Then, each of these objects call `strip_style` on themselves and so
 |      on, recursively until the entire structure has been validated and
 |      stripped.
 |  
 |  to_graph_objs(self)
 |      Walk obj, convert dicts and lists to plotly graph objs.
 |      
 |      For each key in the object, if it corresponds to a special key that
 |      should be associated with a graph object, the ordinary dict or list
 |      will be reinitialized as a special PlotlyDict or PlotlyList of the
 |      appropriate `kind`.
 |  
 |  to_string(self, level=0, indent=4, eol='\n', pretty=True, max_chars=80)
 |      Returns a formatted string showing graph_obj constructors.
 |      
 |              Example:
 |      
 |                  print obj.to_string()
 |      
 |              Keyword arguments:
 |              level (default = 0) -- set number of indentations to start with
 |              indent (default = 4) -- set indentation amount
 |              eol (default = '
 |      ') -- set end of line character(s)
 |              pretty (default = True) -- curtail long list output with a '...'
 |              max_chars (default = 80) -- set max characters per line
 |  
 |  update(self, dict1=None, **dict2)
 |      Update current dict with dict1 and then dict2.
 |      
 |      This recursively updates the structure of the original dictionary-like
 |      object with the new entries in the second and third objects. This
 |      allows users to update with large, nested structures.
 |      
 |      Note, because the dict2 packs up all the keyword arguments, you can
 |      specify the changes as a list of keyword agruments.
 |      
 |      Examples:
 |      # update with dict
 |      obj = Layout(title='my title', xaxis=XAxis(range=[0,1], domain=[0,1]))
 |      update_dict = dict(title='new title', xaxis=dict(domain=[0,.8]))
 |      obj.update(update_dict)
 |      obj
 |      {'title': 'new title', 'xaxis': {'range': [0,1], 'domain': [0,.8]}}
 |      
 |      # update with list of keyword arguments
 |      obj = Layout(title='my title', xaxis=XAxis(range=[0,1], domain=[0,1]))
 |      obj.update(title='new title', xaxis=dict(domain=[0,.8]))
 |      obj
 |      {'title': 'new title', 'xaxis': {'range': [0,1], 'domain': [0,.8]}}
 |      
 |      This 'fully' supports duck-typing in that the call signature is
 |      identical, however this differs slightly from the normal update
 |      method provided by Python's dictionaries.
 |  
 |  validate(self)
 |      Recursively check the validity of the keys in a PlotlyDict.
 |      
 |      The valid keys constitute the entries in each object
 |      dictionary in INFO stored in graph_objs_meta.py.
 |      
 |      The validation process first requires that all nested collections be
 |      converted to the appropriate subclass of PlotlyDict/PlotlyList. Then,
 |      each of these objects call `validate` and so on, recursively,
 |      until the entire object has been validated.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from PlotlyDict:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)
 |  
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from PlotlyDict:
 |  
 |  __metaclass__ = <class 'plotly.graph_objs.graph_objs.DictMeta'>
 |      A meta class for PlotlyDict class creation.
 |      
 |      The sole purpose of this meta class is to properly create the __doc__
 |      attribute so that running help(Obj), where Obj is a subclass of PlotlyDict,
 |      will return information about key-value pairs for that object.
 |  
 |  ----------------------------------------------------------------------
 |  Methods inherited from __builtin__.dict:
 |  
 |  __cmp__(...)
 |      x.__cmp__(y) <==> cmp(x,y)
 |  
 |  __contains__(...)
 |      D.__contains__(k) -> True if D has a key k, else False
 |  
 |  __delitem__(...)
 |      x.__delitem__(y) <==> del x[y]
 |  
 |  __eq__(...)
 |      x.__eq__(y) <==> x==y
 |  
 |  __ge__(...)
 |      x.__ge__(y) <==> x>=y
 |  
 |  __getattribute__(...)
 |      x.__getattribute__('name') <==> x.name
 |  
 |  __getitem__(...)
 |      x.__getitem__(y) <==> x[y]
 |  
 |  __gt__(...)
 |      x.__gt__(y) <==> x>y
 |  
 |  __iter__(...)
 |      x.__iter__() <==> iter(x)
 |  
 |  __le__(...)
 |      x.__le__(y) <==> x<=y
 |  
 |  __len__(...)
 |      x.__len__() <==> len(x)
 |  
 |  __lt__(...)
 |      x.__lt__(y) <==> x<y
 |  
 |  __ne__(...)
 |      x.__ne__(y) <==> x!=y
 |  
 |  __repr__(...)
 |      x.__repr__() <==> repr(x)
 |  
 |  __setitem__(...)
 |      x.__setitem__(i, y) <==> x[i]=y
 |  
 |  __sizeof__(...)
 |      D.__sizeof__() -> size of D in memory, in bytes
 |  
 |  clear(...)
 |      D.clear() -> None.  Remove all items from D.
 |  
 |  copy(...)
 |      D.copy() -> a shallow copy of D
 |  
 |  fromkeys(...)
 |      dict.fromkeys(S[,v]) -> New dict with keys from S and values equal to v.
 |      v defaults to None.
 |  
 |  get(...)
 |      D.get(k[,d]) -> D[k] if k in D, else d.  d defaults to None.
 |  
 |  has_key(...)
 |      D.has_key(k) -> True if D has a key k, else False
 |  
 |  items(...)
 |      D.items() -> list of D's (key, value) pairs, as 2-tuples
 |  
 |  iteritems(...)
 |      D.iteritems() -> an iterator over the (key, value) items of D
 |  
 |  iterkeys(...)
 |      D.iterkeys() -> an iterator over the keys of D
 |  
 |  itervalues(...)
 |      D.itervalues() -> an iterator over the values of D
 |  
 |  keys(...)
 |      D.keys() -> list of D's keys
 |  
 |  pop(...)
 |      D.pop(k[,d]) -> v, remove specified key and return the corresponding value.
 |      If key is not found, d is returned if given, otherwise KeyError is raised
 |  
 |  popitem(...)
 |      D.popitem() -> (k, v), remove and return some (key, value) pair as a
 |      2-tuple; but raise KeyError if D is empty.
 |  
 |  setdefault(...)
 |      D.setdefault(k[,d]) -> D.get(k,d), also set D[k]=d if k not in D
 |  
 |  values(...)
 |      D.values() -> list of D's values
 |  
 |  viewitems(...)
 |      D.viewitems() -> a set-like object providing a view on D's items
 |  
 |  viewkeys(...)
 |      D.viewkeys() -> a set-like object providing a view on D's keys
 |  
 |  viewvalues(...)
 |      D.viewvalues() -> an object providing a view on D's values
 |  
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from __builtin__.dict:
 |  
 |  __hash__ = None
 |  
 |  __new__ = <built-in method __new__ of type object>
 |      T.__new__(S, ...) -> a new object with type S, a subtype of T

In [7]:

# Convert MPL figure to Plotly
my_fig = tls.mpl_to_plotly(fig1)

In [8]:

# Take a quick look at the Plotly representation of the MPL figure
print my_fig.to_string()

Figure(
    data=Data([
        Scatter(
            x=[-2.0, -1.9995999599959997, -1.9991999199919992, -1.998799879...],
            y=[3.7167987868357507e-06, 3.7354293180981496e-06, 3.7541494803...],
            name='$\\sigma = 0.40$',
            mode='lines',
            line=Line(
                dash='solid',
                color='#0000FF',
                width=1.25,
                opacity=1
            ),
            xaxis='x1',
            yaxis='y1'
        ),
        Scatter(
            x=[-2.0, -1.9995999599959997, -1.9991999199919992, -1.998799879...],
            y=[0.0025704649938185107, 0.0025761834897230665, 0.002581913559...],
            name='$\\sigma = 0.60$',
            mode='lines',
            line=Line(
                dash='solid',
                color='#007F00',
                width=1.25,
                opacity=1
            ),
            xaxis='x1',
            yaxis='y1'
        ),
        Scatter(
            x=[-2.0, -1.9995999599959997, -1.9991999199919992, -1.998799879...],
            y=[0.02191037561696068, 0.021937780710823064, 0.021965214590068...],
            name='$\\sigma = 0.80$',
            mode='lines',
            line=Line(
                dash='solid',
                color='#FF0000',
                width=1.25,
                opacity=1
            ),
            xaxis='x1',
            yaxis='y1'
        ),
        Scatter(
            x=[-2.0, -1.9995999599959997, -1.9991999199919992, -1.998799879...],
            y=[0.053990966513188063, 0.054034176567683667, 0.05407741254993...],
            name='$\\sigma = 1.00$',
            mode='lines',
            line=Line(
                dash='solid',
                color='#00BFBF',
                width=1.25,
                opacity=1
            ),
            xaxis='x1',
            yaxis='y1'
        )
    ]),
    layout=Layout(
        width=480,
        height=320,
        autosize=False,
        margin=Margin(
            l=60,
            r=47,
            b=40,
            t=31,
            pad=0
        ),
        hovermode='closest',
        showlegend=False,
        annotations=Annotations([
            Annotation(
                x=0.049865951742627333,
                y=0.79678714859437749,
                text='$y(x)=\\frac{1}{\\sqrt{2\\pi\\sigma^2}}e^{-\\frac{x^2...',
                xref='paper',
                yref='paper',
                showarrow=False,
                font=Font(
                    size=20.0,
                    color='#000000'
                ),
                opacity=1,
                xanchor='left',
                yanchor='bottom'
            ),
            Annotation(
                x=0.84248977989278806,
                y=0.91129032258064524,
                text='$\\sigma = 0.40$',
                xref='paper',
                yref='paper',
                showarrow=False,
                font=Font(
                    size=12.0,
                    color='#000000'
                ),
                opacity=1,
                xanchor='left',
                yanchor='bottom'
            ),
            Annotation(
                x=0.84248977989278806,
                y=0.83198924731182788,
                text='$\\sigma = 0.60$',
                xref='paper',
                yref='paper',
                showarrow=False,
                font=Font(
                    size=12.0,
                    color='#000000'
                ),
                opacity=1,
                xanchor='left',
                yanchor='bottom'
            ),
            Annotation(
                x=0.84248977989278806,
                y=0.75268817204301075,
                text='$\\sigma = 0.80$',
                xref='paper',
                yref='paper',
                showarrow=False,
                font=Font(
                    size=12.0,
                    color='#000000'
                ),
                opacity=1,
                xanchor='left',
                yanchor='bottom'
            ),
            Annotation(
                x=0.84248977989278806,
                y=0.67338709677419362,
                text='$\\sigma = 1.00$',
                xref='paper',
                yref='paper',
                showarrow=False,
                font=Font(
                    size=12.0,
                    color='#000000'
                ),
                opacity=1,
                xanchor='left',
                yanchor='bottom'
            )
        ]),
        xaxis1=XAxis(
            title='$x$',
            domain=[0.0, 1.0],
            range=[-2.0, 2.0],
            type='linear',
            showline=True,
            ticks='inside',
            nticks=9,
            showgrid=False,
            zeroline=False,
            titlefont=Font(
                size=18.0,
                color='#000000'
            ),
            tickfont=Font(
                size=10.0
            ),
            anchor='y1',
            side='bottom',
            mirror='ticks'
        ),
        yaxis1=YAxis(
            title='$y(x)$',
            domain=[0.0, 1.0],
            range=[0.0, 1.0],
            type='linear',
            showline=True,
            ticks='inside',
            nticks=6,
            showgrid=False,
            zeroline=False,
            titlefont=Font(
                size=18.0,
                color='#000000'
            ),
            tickfont=Font(
                size=10.0
            ),
            anchor='x1',
            side='left',
            mirror='ticks'
        )
    )
)

In [9]:

# Strip out the data from the Figure
my_data = my_fig.get_data()
my_data

Out[9]:

{'data': [{'name': '$\\sigma = 0.40$',
   'x': [-2.0,
    -1.9995999599959997,
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    -1.9987998799879989,
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 'layout': [{}, {}, {}, {}, {}]}

As we saw above, the plot is close to a clean translation. Now if I want to use Plotly's defaults, I can use strip_style; see the quick method reference in help(Figure) for more.

In [10]:

py.iplot_mpl(fig1, strip_style = True) 

I can now share and edit the graph in a GUI. My collaborators and I can also analyze and add data from Excel or by copy and pasting. We can style the plot and share the plot (like a Google Doc). You can download plotly to embed in papers, or embed plots in an iframe on your blog or website. To add $LaTeX$ to Plotly graphs, just add a $ on both sides of your expression in your axis labels, titles, or annotations. Plus you can use Plotly to graph formulas.

Now having re-styled it a bit, and shared it, we can call it back into a Notebook.

In [11]:

tls.embed('MattSundquist', '1559')

To see the new object described in Plotly's declarative syntax, I can visit https://plot.ly/~MattSundquist/1559.py, or add .m, .r, .jl., or .js to the URL. And I can read the figure or data right into my Notebook.

That's translation feature is true of any Plotly graph, regardless of where it was made or how it was made or what language I'm using. You can go from sharing data, files, scripts, screenshots, and emails, to making a Plotly graph. It does it all.

II. $LaTeX$ plots in R¶

Next up, let's use Plotly's R API and the R kernel for IPython. If you'd like to see the executable code and installation, head over to our R API.

In [11]:

library(plotly)

p <- plotly(username="MattSundquist", key="c6393zudwx")

trace1 <- list(
  x = c(1, 2, 3, 4), 
  y = c(1, 4, 9, 16), 
  name = "$\alpha_{1c} = 352 \pm 11 \text{ km s}^{-1}$", 
  type = "scatter"
)
trace2 <- list(
  x = c(1, 2, 3, 4), 
  y = c(0.5, 2, 4.5, 8), 
  name = "$\beta_{1c} = 25 \pm 11 \text{ km s}^{-1}$", 
  type = "scatter"
)
data <- list(trace1, trace2)
layout <- list(
  xaxis = list(title = "$\sqrt{(n_\text{c}(t|{T_\text{early}}))}$"), 
  yaxis = list(title = "$d, r \text{ (solar radius)}$")
)

response <- p$plotly(data, kwargs=list(layout=layout, filename="latex", fileopt="overwrite"))
url <- response$url
filename <- response$filename

In [12]:

tls.embed('MattSundquist', '613')

III. $LaTeX$ plots in MATLAB¶

Next up, a plot from Plotly's MATLAB API. To see the code and installation, head over to our MATLAB API.

In [12]:

zeta = [0.01 .02 0.05 0.1 .2 .5 1 ];
colors = ['r' 'g' 'b' 'c' 'm' 'y' 'k'];

w = logspace(-1, 1, 1000);

figure;
for i = 1:7
    a = w.^2 - 1;
    b = 2*w*zeta(i);
    gain = sqrt(1./(a.^2 + b.^2));
    loglog(w, gain, 'color', colors(i), 'linewidth', 2);
    hold on;
end

% Set the axis limits
axis([0.1 10 0.01 100]);

% title and labels
title('|G|(\omega) vs \omega');
xlabel('\omega');
ylabel('|G|(\omega)');

fig2plotly()

In [13]:

tls.embed('MATLAB-Demos', '4')

We're at @plotlygraphs. Head over to our site to get started.

In [14]:

# CSS styling within IPython notebook
from IPython.core.display import HTML
import urllib2
def css_styling():
    url = 'https://raw.githubusercontent.com/plotly/python-user-guide/master/custom.css'
    styles = urllib2.urlopen(url).read()
    return HTML(styles)

css_styling()

Out[14]:

LaTeXLaTeX PLots with MATLAB, Python, R, and a GUI

I. LaTeXLaTeX and matplotlib¶

II. LaTeXLaTeX plots in R¶

III. LaTeXLaTeX plots in MATLAB¶

$LaTeX$ PLots with
MATLAB, Python, R, and a GUI

I. $LaTeX$ and matplotlib¶

II. $LaTeX$ plots in R¶

III. $LaTeX$ plots in MATLAB¶