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matplotlib2tikz.py
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# -*- coding: utf-8 -*-
# ==============================================================================
#
# Copyright (C) 2010--2012 Nico Schlömer
#
# This file is part of matplotlib2tikz.
#
# matplotlib2tikz is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# matplotlib2tikz is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with matplotlib2tikz. If not, see <http://www.gnu.org/licenses/>.
#
# ==============================================================================
'''Script to convert Matplotlib generated figures into TikZ/Pgfplots figures.
'''
# ==============================================================================
# imported modules
import matplotlib as mpl
import numpy as np
import types
import os
import matplotlib.transforms
# ==============================================================================
# meta info
__author__ = 'Nico Schlömer'
__copyright__ = 'Copyright (c) 2010--2012, Nico Schlömer <[email protected]>'
__credits__ = []
__license__ = 'GNU Lesser General Public License (LGPL), Version 3'
__version__ = '0.1.0'
__maintainer__ = 'Nico Schlömer'
__email__ = '[email protected]'
__status__ = 'Development'
# ==============================================================================
def save( filepath,
encoding = None,
figurewidth = None,
figureheight = None,
textsize = 10.0,
tex_relative_path_to_data = None,
strict = False,
draw_rectangles = False,
wrap = True,
extra = None,
show_info = True
):
'''Main function. Here, the recursion into the image starts and the contents
are picked up. The actual file gets written in this routine.
:param filepath: The file to which the TikZ output will be written.
:type filepath: str.
:param encoding: Which encoding to use for the file.
:param figurewidth: If not ``None``, this will be used as figure width
within the TikZ/Pgfplot output. If ``figureheight``
is not given, ``matplotlib2tikz`` will try to preserve
the original width/height ratio.
Note that ``figurewidth`` can be a string literal,
such as ``'\\figurewidth'``.
:type figurewidth: str.
:param figureheight: If not ``None``, this will be used as figure height
within the TikZ/Pgfplot output. If ``figurewidth`` is
not given, ``matplotlib2tikz`` will try to preserve the
original width/height ratio.
Note that ``figurewidth`` can be a string literal,
such as ``'\\figureheight'``.
:type figureheight: str.
:param textsize: The text size (in pt) that the target latex document is using.
Default is 10.0.
:type textsize: float.
:param tex_relative_path_to_data: In some cases, the TikZ file will have to
refer to another file, e.g., a PNG for
image plots. When ``\\input`` into a
regular LaTeX document, the additional
file is looked for in a folder relative
to the LaTeX file, not the TikZ file.
This arguments optionally sets the
relative path from the LaTeX file to the
data.
:type tex_relative_path_to_data: str.
:param strict: Whether or not to strictly stick to matplotlib's appearance.
This influences, for example, whether tick marks are set
exactly as in the matplotlib plot, or if TikZ/Pgfplots
can decide where to put the ticks.
:type strict: bool.
:param draw_rectangles: Whether or not to draw Rectangle objects.
You normally don't want that as legend, axes, and
other entities which are natively taken care of by
Pgfplots are represented as rectangles in
matplotlib. Some plot types (such as bar plots)
cannot otherwise be represented though.
Don't expect working or clean output when using
this option.
:type draw_rectangles: bool.
:param wrap: Whether ``'\\begin{tikzpicture}'`` and ``'\\end{tikzpicture}'``
will be written. One might need to provide custom arguments to
the environment (eg. scale= etc.). Default is ``True``
:type wrap: bool.
:param extra: Extra axis options to be passed (as a dict) to pgfplots.
Default is ``None``.
:type extra: dict.
:returns: None.
The following optional attributes of matplotlib's objects are recognized and handled:
- axes.Axes._matplotlib2tikz_anchors
This attribute can be set to a list of ((x,y), anchor_name) tuples. Invisible nodes
at the respective location will be created which can be referenced from outside
the axis environment.
'''
data = {}
data['fwidth'] = figurewidth
data['fheight'] = figureheight
data['rel data path'] = tex_relative_path_to_data
data['output dir'] = os.path.dirname(filepath)
data['strict'] = strict
data['draw rectangles'] = draw_rectangles
data['tikz libs'] = set()
data['pgfplots libs'] = set()
data['font size'] = textsize
data['custom colors'] = {}
if extra:
data['extra axis options'] = extra
else:
data['extra axis options'] = set()
# open file
import codecs
file_handle = codecs.open(filepath, 'w', encoding)
if show_info:
print('file encoding: {0}'.format(file_handle.encoding))
# gather the file content
data, content = _handle_children( data, mpl.pyplot.gcf() )
disclaimer = ( 'This file was created by matplotlib v%s.\n'
+ '%s\n'
+ 'All rights reserved.\n' ) \
% (__version__, __copyright__)
if show_info:
disclaimer += '\nThe lastest updates can be retrieved from\n\n' \
+ 'https://github.com/nschloe/matplotlib2tikz\n\n' \
+ 'where you can also submit bug reports and leave' \
+ 'comments.\n'
# write disclaimer to the file header
file_handle.write(_tex_comment(disclaimer))
# write the contents
if wrap:
file_handle.write( '\n\\begin{tikzpicture}\n\n' )
coldefs = _get_color_definitions( data )
if coldefs:
file_handle.write( '\n'.join( coldefs ) )
file_handle.write( '\n\n' )
file_handle.write( ''.join(content) )
if wrap:
file_handle.write( '\\end{tikzpicture}' )
# close file
file_handle.close()
# print message about necessary pgfplot libs to command line
if show_info:
_print_pgfplot_libs_message( data )
return
# ==============================================================================
def _tex_comment( comment ):
'''Prepends each line in string with the LaTeX comment key, '%'.
'''
return '% ' + str.replace(comment, '\n', '\n% ') + "\n"
# ==============================================================================
def _print_tree( obj, indent = '' ):
'''Recursively prints the tree structure of the matplotlib object.
'''
print(indent, type(obj))
for child in obj.get_children():
_print_tree( child, indent + ' ' )
return
# ==============================================================================
def _get_color_definitions( data ):
'''Returns the list of custom color definitions for the TikZ file.
'''
definitions = []
for name, rgb in data['custom colors'].items():
definitions.append( '\\definecolor{%s}{rgb}{%.15g,%.15g,%.15g}' % \
(name, rgb[0], rgb[1], rgb[2])
)
return definitions
# ==============================================================================
#def _parse_text( text ):
#'''Parses input text for LaTeX expressions and escaptes them if
#necessary.'''
#replace_list = ['_', '$', '\\', '%' ]
#return
# ==============================================================================
def _draw_axes( data, obj ):
'''Returns the Pgfplots code for an axis environment.
'''
content = []
# Are we dealing with an axis that hosts a colorbar?
# Skip then.
# TODO instead of testing here, rather blacklist the colorbar axis
# plots as soon as they have been found, e.g., by
# _find_associated_colorbar()
if _extract_colorbar(obj):
return
# instantiation
nsubplots = 1
subplot_index = 0
is_subplot = False
if isinstance( obj, mpl.axes.Subplot ):
geom = obj.get_geometry()
nsubplots = geom[0]*geom[1]
if nsubplots > 1:
is_subplot = True
subplot_index = geom[2]
if subplot_index == 1:
content.append( '\\begin{groupplot}[group style=' \
'{group size=%.d by %.d}]\n' % (geom[1],geom[0])
)
data['pgfplots libs'].add( 'groupplots' )
axis_options = []
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# check if axes need to be displayed at all
if not obj.axison:
axis_options.append( 'hide x axis' )
axis_options.append( 'hide y axis' )
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# get plot title
title = obj.get_title()
if title:
axis_options.append( 'title={' + title + '}' )
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# get axes titles
xlabel = obj.get_xlabel()
if xlabel:
axis_options.append( 'xlabel={' + xlabel + '}' )
ylabel = obj.get_ylabel()
if ylabel:
axis_options.append( 'ylabel={' + ylabel + '}' )
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Axes limits.
# Sort the limits so make sure that the smaller of the two is actually
# *min.
xlim = sorted( list( obj.get_xlim() ) )
axis_options.append( 'xmin=%.15g' % xlim[0]
+ ', xmax=%.15g' % xlim[1] )
ylim = sorted( list( obj.get_ylim() ) )
axis_options.append( 'ymin=%.15g' % ylim[0]
+ ', ymax=%.15g' % ylim[1] )
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# axes scaling
xscale = obj.get_xscale()
yscale = obj.get_yscale()
if xscale == 'log' and yscale == 'log':
env = 'loglogaxis'
elif xscale == 'log':
env = 'semilogxaxis'
elif yscale == 'log':
env = 'semilogyaxis'
else:
env = 'axis'
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if not obj.get_axisbelow():
axis_options.append( 'axis on top' )
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# aspect ratio, plot width/height
aspect = obj.get_aspect()
if aspect == 'auto' or aspect == 'normal':
aspect_num = None # just take the given width/height values
elif aspect == 'equal':
aspect_num = 1.0
else:
try:
aspect_num = float(aspect)
except ValueError:
print('Aspect ratio not a number?!')
if data['fwidth'] and data['fheight']:
# width and height overwrite aspect ratio
axis_options.append( 'width='+data['fwidth'] )
axis_options.append( 'height='+data['fheight'] )
elif data['fwidth']:
# only data['fwidth'] given. calculate height by the aspect ratio
axis_options.append( 'width='+data['fwidth'] )
if aspect_num:
alpha = aspect_num * (ylim[1]-ylim[0])/(xlim[1]-xlim[0])
if alpha != 1.0:
# Concatenate the literals, as data['fwidth'] could as well be
# a LaTeX length variable such as \figurewidth.
data['fheight'] = str(alpha) + '*' + data['fwidth']
else:
data['fheight'] = data['fwidth']
axis_options.append( 'height='+data['fheight'] )
elif data['fheight']:
# only data['fheight'] given. calculate width by the aspect ratio
axis_options.append( 'height='+data['fheight'] )
if aspect_num:
alpha = aspect_num * (ylim[1]-ylim[0])/(xlim[1]-xlim[0])
if alpha != 1.0:
# Concatenate the literals, as data['fheight'] could as well be
# a LaTeX length variable such as \figureheight.
data['fwidth'] = str(1.0/alpha) + '*' + data['fheight']
else:
data['fwidth'] = data['fheight']
axis_options.append( 'width='+data['fwidth'] )
else:
if aspect_num:
print('Non-automatic aspect ratio demanded, but neither height '
'nor width of the plot are given. Discard aspect ratio.')
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# get ticks
axis_options.extend( _get_ticks( data, 'x', obj.get_xticks(),
obj.get_xticklabels() ) )
axis_options.extend( _get_ticks( data, 'y', obj.get_yticks(),
obj.get_yticklabels() ) )
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Don't use get_{x,y}gridlines for gridlines; see discussion on
# <http://sourceforge.net/mailarchive/forum.php?thread_name=AANLkTima87pQkZmJhU2oNb8uxD2dfeV-Pa-uXWAFc2-v%40mail.gmail.com&forum_name=matplotlib-users>
# Coordinate of the lines are entirely meaningless, but styles (colors,...
# are respected.
if obj.xaxis._gridOnMajor:
axis_options.append('xmajorgrids')
elif obj.xaxis._gridOnMinor:
axis_options.append('xminorgrids')
if obj.yaxis._gridOnMajor:
axis_options.append('ymajorgrids')
elif obj.yaxis._gridOnMinor:
axis_options.append('yminorgrids')
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# find color bar
colorbar = _find_associated_colorbar( obj )
if colorbar:
colorbar_styles = []
orientation = colorbar.orientation
limits = colorbar.get_clim()
if orientation == 'horizontal':
axis_options.append('colorbar horizontal')
colorbar_ticks = colorbar.ax.get_xticks()
axis_limits = colorbar.ax.get_xlim()
# In matplotlib, the colorbar color limits are determined by
# get_clim(), and the tick positions are as usual with respect to
# {x,y}lim. In Pgfplots, however, they are mixed together.
# Hence, scale the tick positions just like {x,y}lim are scaled
# to clim.
colorbar_ticks = (colorbar_ticks - axis_limits[0]) \
/ (axis_limits[1] - axis_limits[0]) \
* (limits[1] - limits[0]) \
+ limits[0]
# Getting the labels via get_* might not actually be suitable:
# they might not reflect the current state.
# http://sourceforge.net/mailarchive/message.php?msg_name=AANLkTikdNFwSAhMIlLjnd4Ai8-XIdJYGmrwq6PrHkbgi%40mail.gmail.com
colorbar_ticklabels = colorbar.ax.get_xticklabels()
colorbar_styles.extend( _get_ticks( data, 'x', colorbar_ticks,
colorbar_ticklabels ) )
elif orientation == 'vertical':
axis_options.append( 'colorbar' )
colorbar_ticks = colorbar.ax.get_yticks()
axis_limits = colorbar.ax.get_ylim()
# In matplotlib, the colorbar color limits are determined by
# get_clim(), and the tick positions are as usual with respect to
# {x,y}lim. In Pgfplots, however, they are mixed together.
# Hence, scale the tick positions just like {x,y}lim are scaled
# to clim.
colorbar_ticks = (colorbar_ticks - axis_limits[0]) \
/ (axis_limits[1] - axis_limits[0]) \
* (limits[1] - limits[0]) \
+ limits[0]
# Getting the labels via get_* might not actually be suitable:
# they might not reflect the current state.
# http://sourceforge.net/mailarchive/message.php?msg_name=AANLkTikdNFwSAhMIlLjnd4Ai8-XIdJYGmrwq6PrHkbgi%40mail.gmail.com
colorbar_ticklabels = colorbar.ax.get_yticklabels()
colorbar_styles.extend( _get_ticks( data, 'y', colorbar_ticks,
colorbar_ticklabels ) )
else:
raise RuntimeError('Unknown color bar orientation ''%s''. Abort.' %
orientation )
mycolormap, is_custom_cmap = _mpl_cmap2pgf_cmap( colorbar.get_cmap() )
if is_custom_cmap:
axis_options.append( 'colormap=' + mycolormap )
else:
axis_options.append( 'colormap/' + mycolormap )
axis_options.append( 'point meta min=%.15g' % limits[0] )
axis_options.append( 'point meta max=%.15g' % limits[1] )
if colorbar_styles:
axis_options.append( 'colorbar style={%s}' % ','.join(colorbar_styles) )
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# actually print the thing
if is_subplot:
content.append( '\\nextgroupplot' )
else:
content.append( '\\begin{%s}' % env )
# Run through the children objects, gather the content, and give them the
# opportunity to contribute to data['extra axis options'].
data, children_content = _handle_children( data, obj )
if data['extra axis options']:
axis_options.extend( data['extra axis options'] )
if axis_options:
options = ',\n'.join( axis_options )
content.append( '[\n' + options + '\n]\n' )
content.extend( children_content )
# anchors
if hasattr(obj, "_matplotlib2tikz_anchors"):
try:
for coord, anchor_name in obj._matplotlib2tikz_anchors:
content.append('\\node (%s) at (axis cs:%e,%e) {};\n' % (anchor_name, coord[0], coord[1]))
except:
print("Axes attribute _matplotlib2tikz_anchors wrongly set: Expected a list of ((x,y), anchor_name), got '%s'" % str(obj._matplotlib2tikz_anchors))
if not is_subplot:
content.append( '\\end{%s}\n\n' % env )
elif is_subplot and nsubplots == subplot_index:
content.append( '\\end{groupplot}\n\n' )
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
return data, content
# ==============================================================================
def _get_ticks( data, xy, ticks, ticklabels ):
'''
Gets a {'x','y'}, a number of ticks and ticks labels, and returns the
necessary axis options for the given configuration.
'''
axis_options = []
pgfplots_ticks = []
pgfplots_ticklabels = []
is_label_necessary = False
for (tick, ticklabel) in zip(ticks, ticklabels):
pgfplots_ticks.append( tick )
# store the label anyway
label = ticklabel.get_text()
pgfplots_ticklabels.append( label )
# Check if the label is necessary.
# If *one* of the labels is, then all of them must
# appear in the TikZ plot.
is_label_necessary = (label and label != str(tick))
# TODO This seems not quite to be the test whether labels are necessary.
# Leave the ticks to Pgfplots if not in STRICT mode and if there are no
# explicit labels.
if data['strict'] or is_label_necessary:
if pgfplots_ticks:
axis_options.append( '%stick={%s}' % \
( xy,
','.join(['%.15g' % el for el in pgfplots_ticks]) )
)
else:
axis_options.append( '%stick=\\empty' % xy )
if is_label_necessary:
axis_options.append( '%sticklabels={%s}' % \
( xy, ','.join( pgfplots_ticklabels ) )
)
return axis_options
# ==============================================================================
def _mpl_cmap2pgf_cmap( cmap ):
'''Converts a color map as given in matplotlib to a color map as represented
in Pgfplots.
'''
if not isinstance( cmap, mpl.colors.LinearSegmentedColormap ):
print('Don''t know how to handle color map. Using ''blackwhite''.')
is_custom_colormap = False
return ('blackwhite', is_custom_colormap)
if cmap.is_gray():
is_custom_colormap = False
return ('blackwhite', is_custom_colormap)
# For an explanation of what _segmentdata contains, see
# http://matplotlib.sourceforge.net/examples/pylab_examples/custom_cmap.html
# A key sentence:
# If there are discontinuities, then it is a little more complicated.
# Label the 3 elements in each row in the cdict entry for a given color as
# (x, y0, y1). Then for values of x between x[i] and x[i+1] the color
# value is interpolated between y1[i] and y0[i+1].
segdata = cmap._segmentdata
red = segdata['red']
green = segdata['green']
blue = segdata['blue']
# Loop over the data, stop at each spot where the linear
# interpolations is interrupted, and set a color mark there.
#
# Set initial color.
k_red = 0
k_green = 0
k_blue = 0
x = 0.0
colors = []
X = []
while True:
# find next x
x = min( red[k_red][0], green[k_green][0], blue[k_blue][0] )
if red[k_red][0] == x:
red_comp = red[k_red][1]
k_red += 1
else:
red_comp = _linear_interpolation( x,
( red[k_red-1][0],
red[k_red] [0] ),
( red[k_red-1][2],
red[k_red] [1] )
)
if green[k_green][0] == x:
green_comp = green[k_green][1]
k_green += 1
else:
green_comp = _linear_interpolation( x,
( green[k_green-1][0],
green[k_green] [0] ),
( green[k_green-1][2],
green[k_green] [1] )
)
if blue[k_blue][0] == x:
blue_comp = blue[k_blue][1]
k_blue += 1
else:
blue_comp = _linear_interpolation( x,
( blue[k_blue-1][0],
blue[k_blue] [0] ),
( blue[k_blue-1][2],
blue[k_blue] [1] )
)
X.append(x)
colors.append( (red_comp, green_comp, blue_comp) )
if x >= 1.0:
break
# The Pgfplots color map has an actual physical scale, like
# (0cm,10cm), and the points where the colors change is also given
# in those units. As of now (2010-05-06) it is crucial for Pgfplots
# that the difference between two successive points is an integer
# multiple of a given unity (parameter to the colormap; e.g., 1cm).
# At the same time, TeX suffers from significant round-off errors,
# so make sure that this unit is not too small such that the round-
# off errors don't play much of a role. A unit of 1pt, e.g., does
# most often not work.
unit = 'pt'
# Scale to integer
X = _scale_to_int( np.array(X) )
color_changes = []
for (k, x) in enumerate(X):
color_changes.append( 'rgb(%d%s)=(%.15g,%.15g,%.15g)' % \
( (x, unit) + colors[k] )
)
colormap_string = '{mymap}{[1%s] %s}' % \
( unit, '; '.join( color_changes ) )
is_custom_colormap = True
return ( colormap_string, is_custom_colormap )
# ==============================================================================
def _scale_to_int( X ):
'''
Scales the array X such that it contains only integers.
'''
X = X / _gcd_array( X )
return [int(entry) for entry in X]
# ==============================================================================
def _gcd_array( X ):
'''
Return the largest real value h such that all elements in x are integer
multiples of h.
'''
greatest_common_divisor = 0.0
for x in X:
greatest_common_divisor = _gcd( greatest_common_divisor, x )
return greatest_common_divisor
# ==============================================================================
def _gcd( a, b ):
'''Euclidean algorithm for calculating the GCD of two numbers a, b.
This algoritm also works for real numbers:
Find the greatest number h such that a and b are integer multiples of h.
'''
# Keep the tolerance somewhat significantly above machine precision
# as otherwise round-off errors will be accounted for, returning 1.0e-10
# instead of 1.0 for the values
# [ 1.0, 2.0000000001, 3.0, 4.0 ].
while a > 1.0e-5:
a, b = b % a, a
return b
# ==============================================================================
def _linear_interpolation( x, X, Y ):
'''Given two data points [X,Y], linearly interpolate those at x.
'''
return ( Y[1]*(x-X[0]) + Y[0]*(X[1]-x) ) / ( X[1]-X[0] )
# ==============================================================================
def _transform_to_data_coordinates(obj, xdata, ydata):
'''Transform to data coordinates
The coordinates might not be in data coordinates, but could be partly in axes coordinates.
For example, the matplotlib command
axes.axvline(2)
will have the y coordinates set to 0 and 1, not to the limits. Therefore, a two stage transform is to
be applied, first transforming to display coordinates, then from display to data.
In case of problems (non-invertible, or whatever), print a warning and continue anyways.
'''
try:
points = zip(xdata, ydata)
transform = matplotlib.transforms.composite_transform_factory(obj.get_transform(), obj.get_axes().transData.inverted())
points_data = transform.transform(points)
xdata, ydata = zip(*points_data)
except:
print("Problem during transformation, continuing with original data")
return (xdata, ydata)
# ==============================================================================
TIKZ_LINEWIDTHS = { 0.1: 'ultra thin',
0.2: 'very thin',
0.4: 'thin',
0.6: 'semithick',
0.8: 'thick',
1.2: 'very thick',
1.6: 'ultra thick' }
# ------------------------------------------------------------------------------
def _draw_line2d( data, obj ):
'''Returns the Pgfplots code for an Line2D environment.
'''
content = []
addplot_options = []
# --------------------------------------------------------------------------
# get the linewidth (in pt)
line_width = obj.get_linewidth()
if data['strict']:
# Takes the matplotlib linewidths, and just translate them
# into Pgfplots.
try:
addplot_options.append( TIKZ_LINEWIDTHS[ line_width ] )
except KeyError:
# explicit line width
addplot_options.append( 'line width=%spt' % line_width )
else:
# The following is an alternative approach to line widths.
# The default line width in matplotlib is 1.0pt, in Pgfplots 0.4pt
# ('thin').
# Match the two defaults, and scale for the rest.
scaled_line_width = line_width / 1.0 # scale by default line width
if scaled_line_width == 0.25:
addplot_options.append( 'ultra thin' )
elif scaled_line_width == 0.5:
addplot_options.append( 'very thin' )
elif scaled_line_width == 1.0:
pass # Pgfplots default line width, 'thin'
elif scaled_line_width == 1.5:
addplot_options.append( 'semithick' )
elif scaled_line_width == 2:
addplot_options.append( 'thick' )
elif scaled_line_width == 3:
addplot_options.append( 'very thick' )
elif scaled_line_width == 4:
addplot_options.append( 'ultra thick' )
else:
# explicit line width
addplot_options.append( 'line width=%rpt' % (0.4*line_width) )
# --------------------------------------------------------------------------
# get line color
color = obj.get_color()
data, line_xcolor, _ = _mpl_color2xcolor( data, color )
addplot_options.append( line_xcolor )
alpha = obj.get_alpha()
if alpha != None :
addplot_options.append( 'opacity=%r' % alpha)
show_line, linestyle = _mpl_linestyle2pgfp_linestyle( obj.get_linestyle() )
if show_line and linestyle:
addplot_options.append( linestyle )
marker_face_color = obj.get_markerfacecolor()
marker_edge_color = obj.get_markeredgecolor()
data, marker, extra_mark_options = \
_mpl_marker2pgfp_marker( data, obj.get_marker(), marker_face_color )
if marker:
addplot_options.append( 'mark=' + marker )
mark_size = obj.get_markersize()
if mark_size:
# setting half size because pgfplots counts the radius/half-width
pgf_size = int( mark_size/2 )
# make sure we didn't round off to zero by accident
if pgf_size == 0 and mark_size != 0:
pgf_size = 1
addplot_options.append( 'mark size=%d' % pgf_size)
mark_options = []
if extra_mark_options:
mark_options.append( extra_mark_options )
if marker_face_color:
data, face_xcolor, _ = _mpl_color2xcolor( data, marker_face_color )
if face_xcolor != line_xcolor:
mark_options.append( 'fill=' + face_xcolor )
if marker_edge_color and marker_edge_color != marker_face_color:
data, draw_xcolor, _ = _mpl_color2xcolor( data, marker_edge_color )
if draw_xcolor != line_xcolor:
mark_options.append( 'draw=' + draw_xcolor )
if mark_options:
addplot_options.append( 'mark options={%s}' % ','.join(mark_options)
)
if marker and not show_line:
addplot_options.append( 'only marks' )
# process options
content.append( '\\addplot ' )
if addplot_options:
options = ', '.join( addplot_options )
content.append( '[' + options + ']\n' )
content.append( 'coordinates {\n' )
# print the hard numerical data
xdata, ydata = _transform_to_data_coordinates(obj, *obj.get_data())
try:
has_mask = ydata.mask.any()
except AttributeError:
has_mask = 0
if has_mask:
# matplotlib jumps at masked images, while Pgfplots by default
# interpolates. Hence, if we have a masked plot, make sure that Pgfplots
# jump as well.
data['extra axis options'].add( 'unbounded coords=jump' )
for (x, y, is_masked) in zip(xdata, ydata, ydata.mask):
if is_masked:
content.append( '(%.15g,nan) ' % x )
else:
content.append( '(%.15g,%.15g) ' % (x, y) )
else:
for (x, y) in zip(xdata, ydata):
content.append( '(%.15g,%.15g)\n' % (x, y) )
content.append( '\n};\n' )
return data, content
# ==============================================================================
# for matplotlib markers, see
# http://matplotlib.sourceforge.net/api/artist_api.html#matplotlib.lines.Line2D.set_marker
MP_MARKER2PGF_MARKER = { '.' : '*', # point
'o' : 'o', # circle
'+' : '+', # plus
'x' : 'x', # x
'None': None,
' ' : None,
'' : None
}
# the following markers are only available with PGF's plotmarks library
MP_MARKER2PLOTMARKS = { 'v' : ('triangle', 'rotate=180'), # triangle down
'1' : ('triangle', 'rotate=180'),
'^' : ('triangle', None), # triangle up
'2' : ('triangle', None),
'<' : ('triangle', 'rotate=270'), # triangle left
'3' : ('triangle', 'rotate=270'),
'>' : ('triangle', 'rotate=90'), # triangle right
'4' : ('triangle', 'rotate=90'),
's' : ('square', None),
'p' : ('pentagon', None),
'*' : ('asterisk', None),
'h' : ('star', None), # hexagon 1
'H' : ('star', None), # hexagon 2
'd' : ('diamond', None), # diamond
'D' : ('diamond', None), # thin diamond
'|' : ('|', None), # vertical line
'_' : ('_', None) # horizontal line
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def _mpl_marker2pgfp_marker( data, mpl_marker, is_marker_face_color ):
'''Translates a marker style of matplotlib to the corresponding style
in Pgfplots.
'''
# try default list
try:
pgfplots_marker = MP_MARKER2PGF_MARKER[ mpl_marker ]
if is_marker_face_color and pgfplots_marker == 'o':
pgfplots_marker = '*'
data['pgfplots libs'].add( 'plotmarks' )
marker_options = None
return ( data, pgfplots_marker, marker_options )
except KeyError:
pass
# try plotmarks list
try:
data['pgfplots libs'].add( 'plotmarks' )
pgfplots_marker, marker_options = MP_MARKER2PLOTMARKS[ mpl_marker ]
if (is_marker_face_color and is_marker_face_color.lower() != "none") and not pgfplots_marker in ['|', '_']:
pgfplots_marker += '*'
return ( data, pgfplots_marker, marker_options )
except KeyError:
pass
if mpl_marker == ',': # pixel
print('Unsupported marker ''%r''.' % mpl_marker)
else:
print('Unknown marker ''%r''.' % mpl_marker)
return ( data, None, None )
# ==============================================================================
MPLLINESTYLE_2_PGFPLOTSLINESTYLE = { 'None': None,
'-' : None,
':' : 'dotted',
'--' : 'dashed',
'-.' : 'dash pattern=on 1pt off 3pt ' \
'on 3pt off 3pt'
}
# ------------------------------------------------------------------------------
def _mpl_linestyle2pgfp_linestyle( line_style ):
'''Translates a line style of matplotlib to the corresponding style
in Pgfplots.
'''
show_line = (line_style != 'None')
try:
style = MPLLINESTYLE_2_PGFPLOTSLINESTYLE[ line_style ]
except KeyError:
print('Unknown line style ''%r''. Using default.' % line_style)
style = None
return show_line, style
# ==============================================================================
def _draw_image( data, obj ):
'''Returns the Pgfplots code for an image environment.
'''
content = []
if not 'img number' in data.keys():
data['img number'] = 0
# Make sure not to overwrite anything.
file_exists = True
while file_exists:
data['img number'] = data['img number'] + 1
filename = os.path.join( data['output dir'],
'img' + str(data['img number']) + '.png'
)
file_exists = os.path.isfile( filename )
# store the image as in a file
img_array = obj.get_array()
dims = img_array.shape
if len(dims)==2: # the values are given as one real number: look at cmap
clims = obj.get_clim()
mpl.pyplot.imsave( fname = filename,
arr = img_array ,
cmap = obj.get_cmap(),
vmin = clims[0],
vmax = clims[1] )
elif len(dims) == 3 and dims[2] in [3, 4]:
# RGB (+alpha) information at each point
# convert to PIL image (after upside-down flip)
import Image
image = Image.fromarray(np.flipud(img_array))
image.save( filename )
else:
raise RuntimeError( 'Unable to store image array.' )
# write the corresponding information to the TikZ file
extent = obj.get_extent()
# the format specification will only accept tuples, not lists
if isinstance(extent, list): # convert to () list
extent = tuple(extent)
if data['rel data path']:
rel_filepath = os.path.join(data['rel data path'],
os.path.basename(filename)
)
else:
rel_filepath = os.path.basename(filename)
# Explicitly use \pgfimage as includegrapics command, as the default
# \includegraphics fails unexpectedly in some cases
content.append( '\\addplot graphics [includegraphics cmd=\pgfimage,' \
'xmin=%.15g, xmax=%.15g, ' \
'ymin=%.15g, ymax=%.15g] {%s};\n' % \
( extent + (rel_filepath,) )
)
data, cont = _handle_children( data, obj )
content.extend( cont )
return data, content
# ==============================================================================
def _find_associated_colorbar( obj ):
''' Rather poor way of telling whether an axis has a colorbar associated:
Check the next axis environment, and see if it is de facto a color bar;
if yes, return the color bar object.
'''
for child in obj.get_children():
try:
cbar = child.colorbar
except AttributeError:
continue
if not cbar == None: # really necessary?
# if fetch was successful, cbar contains
# ( reference to colorbar,
# reference to axis containing colorbar )
return cbar[0]
return None
# ==============================================================================
def _is_colorbar( obj ):
'''
Returns 'True' if 'obj' is a color bar, and 'False' otherwise.
'''
# TODO Are the colorbars exactly the l.collections.PolyCollection's?
if isinstance( obj, mpl.collections.PolyCollection ):
arr = obj.get_array()
dims = arr.shape
if len(dims) == 1:
return True # o rly?
else:
return False
else:
return False
# ==============================================================================
def _extract_colorbar( obj ):
'''
Search for color bars as subobjects of obj, and return the first found.
If none is found, return None.
'''
colorbars = mpl.pyplot.findobj( obj, _is_colorbar )
if not colorbars:
return None
if not _equivalent( colorbars ):
print('More than one color bar found. Use first one.')
return colorbars[0]
# ==============================================================================
def _equivalent( array ):
'''
Checks if the vectors consists of all the same objects.
'''
if not array:
return False
else:
for elem in array:
if elem != array[0]:
return False
return True
# ==============================================================================
def _draw_polycollection( data, obj ):
'''Returns Pgfplots code for a number of polygons. Currently empty.
'''
print('matplotlib2tikz: Don''t know how to draw a PolyCollection.')
return data, ''