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MatPlotLib for LaTeX

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This post lays out an end-to-end working version of a script that works on Fedora 22 (at a minimum) for producing matplotlib output that can be directly incorporated into LaTeX documents, and has the right font sizes, etc. without going through a raster-graphics layer.

The intermediate .pgf graphics files are also acceptable to arXiv, and can be loaded up alongside your original .tex source file.

Preparation

On my system that was already set up to produce regular LaTeX documents, the following additional packages

dnf install texlive-xetex texlive-collection-xetex

Basic Imports and Arguments

For stylistic consistency, I chose to have one central script that produces all the graphs required. Simply call it without arguments to produce all the charts for embedding.

Calling it with --latex=0 will instead display a popup window with the output for quicker debugging of the code. And there is an option --figure=XXX that can be used to focus on one figure type only. Doing this speeds up the interation speed tremendously.

import matplotlib.pyplot as plt
import numpy as np

import matplotlib
import math

import argparse

parser = argparse.ArgumentParser(description='')
parser.add_argument('-f','--figure', help='Figure to generate ("xy", None for all)', type=str, default=None)
parser.add_argument('-l','--latex', help='Produce output for LaTex docs', type=int, default=1)
args = parser.parse_args()

LaTeX defaults for matplotlib

This code was essentially taken from :

and modified to bring it up-to-date.

SPINE_COLOR = 'gray'

def savefig(filename):
  if args.latex:
    plt.savefig('figure_{}.pgf'.format(filename))
    #plt.savefig('figure_{}.pdf'.format(filename))
  else:
    plt.show()


def latexify(fig_width=None, fig_height=None):
  if args.latex:
    """Set up matplotlib's RC params for LaTeX plotting.
    Call this before plotting a figure.

    Parameters
    ----------
    fig_width : float, optional, inches
    fig_height : float,  optional, inches
    """

    # code adapted from http://www.scipy.org/Cookbook/Matplotlib/LaTeX_Examples

    # Width and max height in inches for IEEE journals taken from
    # computer.org/cms/Computer.org/Journal%20templates/transactions_art_guide.pdf

    fig_width_pt = 397.48499   # Get this from LaTeX using \the\textwidth
    inches_per_pt = 1.0/72.27  # Convert pt to inch

    if fig_width is None:
        fig_width = fig_width_pt*inches_per_pt

    if fig_height is None:
        golden_mean = (math.sqrt(5)-1.0)/2.0    # Aesthetic ratio
        fig_height = fig_width*golden_mean # height in inches

    MAX_HEIGHT_INCHES = 8.0
    if fig_height > MAX_HEIGHT_INCHES:
        print("WARNING: fig_height too large:" + fig_height +
              "so will reduce to" + MAX_HEIGHT_INCHES + "inches.")
        fig_height = MAX_HEIGHT_INCHES

    params = {
      'backend': 'ps',
      #'text.latex.preamble': ['\usepackage{gensymb}'],
      'axes.labelsize': 8, # fontsize for x and y labels (was 10)
      'axes.titlesize': 8,
      'font.size':       8, # was 10
      'legend.fontsize': 8, # was 10
      'xtick.labelsize': 8,
      'ytick.labelsize': 8,
      'text.usetex': True,
      'figure.figsize': [fig_width,fig_height],
      'font.family': 'serif'
    }

    matplotlib.rcParams.update(params)

def format_axes(ax):
  if args.latex:
    for spine in ['top', 'right']:
        ax.spines[spine].set_visible(False)

    for spine in ['left', 'bottom']:
        ax.spines[spine].set_color(SPINE_COLOR)
        ax.spines[spine].set_linewidth(0.5)

    ax.xaxis.set_ticks_position('bottom')
    ax.yaxis.set_ticks_position('left')

    for axis in [ax.xaxis, ax.yaxis]:
        axis.set_tick_params(direction='out', color=SPINE_COLOR)

  return ax

Code for a matplotlib figure

This is produced (if the command line arguments suggest it should be) by parsing a log file output by one of my training algorithms, so there's some sample log parsing in there.

( note that the b: 0 match is done so specifically because I wanted to capture each epoch's batch=zero, and was in a bit of a hurry... )

The data is plotted against two independent y-axes, and is finally saved (or displayed, depending on --latex=X).

By making use of latexify(), format_axes() and savefig() the whole screen vs page debate is deferred until you can get the graph looking right interactively.

Note that mathmatical notation can be included in any of the text - just introduce within the $ context - and make sure that \ is doubled up - Python will 'use up' the first level of escaping.

if args.figure is None or args.figure == 'frog':
  epoch=[]
  l2=[]
  var=[]
  mix=[]

  filename = '../src/4-sparse/screenlog.0-v13'
  print("Reading from  ", filename)
  with open(filename) as f:
    for l in f.readlines():
      #  epoch:   0, b:      0, l2:    168.6449, sparsity_mean_:0.1805, sigma: 0.00, mix: 0.00,
      m = re.match("\s*epoch: +(\d+), b:      0, l2: +(\d+\.\d+), sparsity_mean_: *(\d+\.\d+), sigma: *(\d+\.\d+), mix: *(\d+\.\d+),", l)
      if m:
        epoch.append( int(m.group(1)) )
        l2.append( float(m.group(2))/1000 )
        var.append( float(m.group(3))/4 )
        mix.append( float(m.group(5))/100*2.5 )

  latexify()

  fig = plt.figure()
  ax = fig.add_subplot(111)
  ax.set_xlabel("Epoch number")

  ax.plot(epoch,l2, color='b',   label = '$L2$ error')
  ax.set_ylim([0,50.0/1000])
  ax.legend(loc=2)
  ax.set_ylabel("mean $L2$ error")

  ax2 = ax.twinx()
  ax2.plot(epoch,var, color='r', label = 'Variance of $\\mathbf{B^*}$')
  ax2.plot(epoch,mix, color='g', linestyle='--', label = '$\\lambda$ parameter')
  ax2.set_ylim([0,0.25])
  ax2.legend(loc=1)

  ax = format_axes(ax)

  savefig('frog')

Code for the LaTeX document itself

Near the top of your .tex file you'll need to include the pgf package :

\usepackage{pgf}

And the figure itself can be included (and referred to) as follows :

\begin{figure}
 \begin{center}
  \input{figure_frog.pgf}
 \end{center}
 \caption{Performance during training of the quasi auto-encoding network}
 \label{figure:frog}
\end{figure}

Figure \ref{figure:frog} shows the performance during training ...
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