Matplotlib style that I use for my master thesis

Thesis Matplotlib Style.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "first you need to copy the `*.mplstyle` to your home directory.\n",
    "\n",
    "\n",
    "In Mac/Linux, it is in `~/.matplotlib/stylelib` directory.\n",
    "\n",
    "The style should be listed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['seaborn-dark',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-ticks',\n",
       " 'fivethirtyeight',\n",
       " 'seaborn-whitegrid',\n",
       " 'classic',\n",
       " '_classic_test',\n",
       " 'fast',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-pastel',\n",
       " 'grayscale',\n",
       " 'seaborn-notebook',\n",
       " 'ggplot',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-muted',\n",
       " 'seaborn',\n",
       " 'Solarize_Light2',\n",
       " 'seaborn-paper',\n",
       " 'bmh',\n",
       " 'tableau-colorblind10',\n",
       " 'seaborn-white',\n",
       " 'dark_background',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-deep',\n",
       " 'thesis-3col',\n",
       " 'thesis-bw',\n",
       " 'thesis',\n",
       " 'thesis-double',\n",
       " 'dark']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib.style.available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then explicitly set the style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.style.use('thesis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 446.544x275.962 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-np.pi, np.pi, 201)\n",
    "plt.plot(x, np.sin(x), label='sin')\n",
    "plt.plot(x, np.cos(x), label='cos')\n",
    "plt.xlabel('Angle [rad]')\n",
    "plt.ylabel('value')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can override the style by using context, to set the style with BW color by using the `thesis-bw` style, or to use three columns subplot by using the `thesis-3col` style."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 446.544x275.962 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('thesis-bw'):\n",
    "    x = np.linspace(-np.pi, np.pi, 201)\n",
    "    plt.plot(x, np.sin(x), label='sin')\n",
    "    plt.plot(x, np.cos(x), label='cos')\n",
    "    plt.xlabel('Angle [rad]')\n",
    "    plt.ylabel('value')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 446.544x223.2 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('thesis-3col'):\n",
    "    fig, (ax1, ax2, ax3) = plt.subplots(1, 3)\n",
    "    x = np.linspace(-np.pi, np.pi, 201)\n",
    "    ax1.plot(x, np.sin(x))\n",
    "    ax2.plot(x, np.cos(x))\n",
    "    ax3.plot(x, x)\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

thesis-3col.mplstyle

figure.figsize   : 6.202, 3.1   # figure size in inches

thesis-bw.mplstyle

axes.prop_cycle: cycler('color', ['60636A', 'A5ACAF', '414451', '8F8782', 'CFCFCF'])

thesis.mplstyle

### MATPLOTLIBRC FORMAT

# This is a sample matplotlib configuration file - you can find a copy
# of it on your system in
# site-packages/matplotlib/mpl-data/matplotlibrc.  If you edit it
# there, please note that it will be overwritten in your next install.
# If you want to keep a permanent local copy that will not be
# overwritten, place it in the following location:
# unix/linux:
#     $HOME/.config/matplotlib/matplotlibrc or
#     $XDG_CONFIG_HOME/matplotlib/matplotlibrc (if $XDG_CONFIG_HOME is set)
# other platforms:
#     $HOME/.matplotlib/matplotlibrc
#
# See http://matplotlib.org/users/customizing.html#the-matplotlibrc-file for
# more details on the paths which are checked for the configuration file.
#
# This file is best viewed in a editor which supports python mode
# syntax highlighting. Blank lines, or lines starting with a comment
# symbol, are ignored, as are trailing comments.  Other lines must
# have the format
#    key : val # optional comment
#
# Colors: for the color values below, you can either use - a
# matplotlib color string, such as r, k, or b - an rgb tuple, such as
# (1.0, 0.5, 0.0) - a hex string, such as ff00ff or #ff00ff - a scalar
# grayscale intensity such as 0.75 - a legal html color name, e.g., red,
# blue, darkslategray

#### CONFIGURATION BEGINS HERE

# The default backend; one of GTK GTKAgg GTKCairo GTK3Agg GTK3Cairo
# CocoaAgg MacOSX Qt4Agg Qt5Agg TkAgg WX WXAgg Agg Cairo GDK PS PDF SVG
# Template.
# You can also deploy your own backend outside of matplotlib by
# referring to the module name (which must be in the PYTHONPATH) as
# 'module://my_backend'.
#backend      : MacOSX

# If you are using the Qt4Agg backend, you can choose here
# to use the PyQt4 bindings or the newer PySide bindings to
# the underlying Qt4 toolkit.
#backend.qt4 : PyQt4        # PyQt4 | PySide

# Note that this can be overridden by the environment variable
# QT_API used by Enthought Tool Suite (ETS); valid values are
# "pyqt" and "pyside".  The "pyqt" setting has the side effect of
# forcing the use of Version 2 API for QString and QVariant.

# The port to use for the web server in the WebAgg backend.
# webagg.port : 8888

# If webagg.port is unavailable, a number of other random ports will
# be tried until one that is available is found.
# webagg.port_retries : 50

# When True, open the webbrowser to the plot that is shown
# webagg.open_in_browser : True

# When True, the figures rendered in the nbagg backend are created with
# a transparent background.
# nbagg.transparent : True

# if you are running pyplot inside a GUI and your backend choice
# conflicts, we will automatically try to find a compatible one for
# you if backend_fallback is True
#backend_fallback: True

#interactive  : False
#toolbar      : toolbar2   # None | toolbar2  ("classic" is deprecated)
#timezone     : UTC        # a pytz timezone string, e.g., US/Central or Europe/Paris

# Where your matplotlib data lives if you installed to a non-default
# location.  This is where the matplotlib fonts, bitmaps, etc reside
#datapath : /home/jdhunter/mpldata


### LINES
# See http://matplotlib.org/api/artist_api.html#module-matplotlib.lines for more
# information on line properties.
lines.linewidth   : 1.5     # line width in points
#lines.linestyle   : -       # solid line
#lines.color       : blue    # has no affect on plot(); see axes.prop_cycle
#lines.marker      : None    # the default marker
lines.markeredgewidth  : 0.3     # the line width around the marker symbol
lines.markersize  : 4            # markersize, in points
#lines.dash_joinstyle : miter        # miter|round|bevel
#lines.dash_capstyle : butt          # butt|round|projecting
#lines.solid_joinstyle : miter       # miter|round|bevel
#lines.solid_capstyle : projecting   # butt|round|projecting
#lines.antialiased : True         # render lines in antialiased (no jaggies)

#markers.fillstyle: full # full|left|right|bottom|top|none

### PATCHES
# Patches are graphical objects that fill 2D space, like polygons or
# circles.  See
# http://matplotlib.org/api/artist_api.html#module-matplotlib.patches
# information on patch properties
#patch.linewidth        : 1.0     # edge width in points
#patch.facecolor        : blue
#patch.edgecolor        : black
#patch.antialiased      : True    # render patches in antialiased (no jaggies)

### FONT
#
# font properties used by text.Text.  See
# http://matplotlib.org/api/font_manager_api.html for more
# information on font properties.  The 6 font properties used for font
# matching are given below with their default values.
#
# The font.family property has five values: 'serif' (e.g., Times),
# 'sans-serif' (e.g., Helvetica), 'cursive' (e.g., Zapf-Chancery),
# 'fantasy' (e.g., Western), and 'monospace' (e.g., Courier).  Each of
# these font families has a default list of font names in decreasing
# order of priority associated with them.  When text.usetex is False,
# font.family may also be one or more concrete font names.
#
# The font.style property has three values: normal (or roman), italic
# or oblique.  The oblique style will be used for italic, if it is not
# present.
#
# The font.variant property has two values: normal or small-caps.  For
# TrueType fonts, which are scalable fonts, small-caps is equivalent
# to using a font size of 'smaller', or about 83% of the current font
# size.
#
# The font.weight property has effectively 13 values: normal, bold,
# bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as
# 400, and bold is 700.  bolder and lighter are relative values with
# respect to the current weight.
#
# The font.stretch property has 11 values: ultra-condensed,
# extra-condensed, condensed, semi-condensed, normal, semi-expanded,
# expanded, extra-expanded, ultra-expanded, wider, and narrower.  This
# property is not currently implemented.
#
# The font.size property is the default font size for text, given in pts.
# 12pt is the standard value.
#
#font.family         : sans-serif
font.family         : sans-serif
#font.style          : normal
#font.variant        : normal
#font.weight         : medium
#font.stretch        : normal
# note that font.size controls default text sizes.  To configure
# special text sizes tick labels, axes, labels, title, etc, see the rc
# settings for axes and ticks. Special text sizes can be defined
# relative to font.size, using the following values: xx-small, x-small,
# small, medium, large, x-large, xx-large, larger, or smaller
#font.size           : 12.0
font.size           : 12.0
#font.serif          : Bitstream Vera Serif, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif
#font.sans-serif     : Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif
#font.sans-serif     : Arial, Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Helvetica, Avant Garde, sans-serif
#font.cursive        : Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, cursive
#font.fantasy        : Comic Sans MS, Chicago, Charcoal, Impact, Western, Humor Sans, fantasy
#font.monospace      : Bitstream Vera Sans Mono, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace

### TEXT
# text properties used by text.Text.  See
# http://matplotlib.org/api/artist_api.html#module-matplotlib.text for more
# information on text properties

text.color          : (0.145, 0.157, 0.169)

### LaTeX customizations. See http://wiki.scipy.org/Cookbook/Matplotlib/UsingTex
#text.usetex         : False  # use latex for all text handling. The following fonts
                              # are supported through the usual rc parameter settings:
                              # new century schoolbook, bookman, times, palatino,
                              # zapf chancery, charter, serif, sans-serif, helvetica,
                              # avant garde, courier, monospace, computer modern roman,
                              # computer modern sans serif, computer modern typewriter
                              # If another font is desired which can loaded using the
                              # LaTeX \usepackage command, please inquire at the
                              # matplotlib mailing list
#text.latex.unicode : False # use "ucs" and "inputenc" LaTeX packages for handling
                            # unicode strings.
#text.latex.preamble :  # IMPROPER USE OF THIS FEATURE WILL LEAD TO LATEX FAILURES
                            # AND IS THEREFORE UNSUPPORTED. PLEASE DO NOT ASK FOR HELP
                            # IF THIS FEATURE DOES NOT DO WHAT YOU EXPECT IT TO.
                            # preamble is a comma separated list of LaTeX statements
                            # that are included in the LaTeX document preamble.
                            # An example:
                            # text.latex.preamble : \usepackage{bm},\usepackage{euler}
                            # The following packages are always loaded with usetex, so
                            # beware of package collisions: color, geometry, graphicx,
                            # type1cm, textcomp. Adobe Postscript (PSSNFS) font packages
                            # may also be loaded, depending on your font settings

#text.dvipnghack : None      # some versions of dvipng don't handle alpha
                             # channel properly.  Use True to correct
                             # and flush ~/.matplotlib/tex.cache
                             # before testing and False to force
                             # correction off.  None will try and
                             # guess based on your dvipng version

#text.hinting : auto   # May be one of the following:
                       #   'none': Perform no hinting
                       #   'auto': Use freetype's autohinter
                       #   'native': Use the hinting information in the
                       #             font file, if available, and if your
                       #             freetype library supports it
                       #   'either': Use the native hinting information,
                       #             or the autohinter if none is available.
                       # For backward compatibility, this value may also be
                       # True === 'auto' or False === 'none'.
#text.hinting_factor : 8 # Specifies the amount of softness for hinting in the
                         # horizontal direction.  A value of 1 will hint to full
                         # pixels.  A value of 2 will hint to half pixels etc.

#text.antialiased : True # If True (default), the text will be antialiased.
                         # This only affects the Agg backend.

# The following settings allow you to select the fonts in math mode.
# They map from a TeX font name to a fontconfig font pattern.
# These settings are only used if mathtext.fontset is 'custom'.
# Note that this "custom" mode is unsupported and may go away in the
# future.
#mathtext.cal : cursive
#mathtext.rm  : serif
#mathtext.tt  : monospace
#mathtext.it  : serif:italic
#mathtext.bf  : serif:bold
#mathtext.sf  : sans
#mathtext.fontset : cm # Should be 'cm' (Computer Modern), 'stix',
                       # 'stixsans' or 'custom'
#mathtext.fallback_to_cm : True  # When True, use symbols from the Computer Modern
                                 # fonts when a symbol can not be found in one of
                                 # the custom math fonts.

#mathtext.default : it # The default font to use for math.
                       # Can be any of the LaTeX font names, including
                       # the special name "regular" for the same font
                       # used in regular text.

### AXES
# default face and edge color, default tick sizes,
# default fontsizes for ticklabels, and so on.  See
# http://matplotlib.org/api/axes_api.html#module-matplotlib.axes
#axes.hold           : True    # whether to clear the axes by default on
axes.facecolor      : white   # axes background color
axes.edgecolor      : (0.145, 0.157, 0.169)   # axes edge color
#axes.linewidth      : 1.0     # edge linewidth
axes.linewidth      : 0     # edge linewidth
#axes.grid           : False   # display grid or not
axes.grid           : True   # display grid or not
axes.titlesize      : 14   # fontsize of the axes title
axes.labelsize      : 12  # fontsize of the x any y labels
#axes.labelpad       : 5.0     # space between label and axis
#axes.labelweight    : normal  # weight of the x and y labels
axes.labelcolor     : (0.145, 0.157, 0.169)
axes.axisbelow      : True   # whether axis gridlines and ticks are below
                               # the axes elements (lines, text, etc)

#axes.formatter.limits : -7, 7 # use scientific notation if log10
                               # of the axis range is smaller than the
                               # first or larger than the second
#axes.formatter.use_locale : False # When True, format tick labels
                                   # according to the user's locale.
                                   # For example, use ',' as a decimal
                                   # separator in the fr_FR locale.
#axes.formatter.use_mathtext : False # When True, use mathtext for scientific
                                     # notation.
#axes.formatter.useoffset      : True    # If True, the tick label formatter
                                         # will default to labeling ticks relative
                                         # to an offset when the data range is very
                                         # small compared to the minimum absolute
                                         # value of the data.

#axes.unicode_minus  : True    # use unicode for the minus symbol
                               # rather than hyphen.  See
                               # http://en.wikipedia.org/wiki/Plus_and_minus_signs#Character_codes
#axes.prop_cycle    : cycler('color', 'bgrcmyk')
                                            # color cycle for plot lines
                                            # as list of string colorspecs:
                                            # single letter, long name, or
                                            # web-style hex
axes.prop_cycle    : cycler('color', ['1F77B4', 'FF7F0E', '2CA02C', 'D62728', '9467BD', '8C564B', 'E377C2', '7F7F7F', 'BCBD22', '17BECF'])
#axes.xmargin        : 0  # x margin.  See `axes.Axes.margins`
#axes.ymargin        : 0  # y margin See `axes.Axes.margins`

#polaraxes.grid      : True    # display grid on polar axes
#axes3d.grid         : True    # display grid on 3d axes

### TICKS
# see http://matplotlib.org/api/axis_api.html#matplotlib.axis.Tick
#xtick.major.size     : 4      # major tick size in points
#xtick.minor.size     : 2      # minor tick size in points
#xtick.major.width    : 0.5    # major tick width in points
xtick.major.width    : 0    # major tick width in points
#xtick.minor.width    : 0.5    # minor tick width in points
#xtick.major.pad      : 4      # distance to major tick label in points
#xtick.minor.pad      : 4      # distance to the minor tick label in points
xtick.color          : (0.322, 0.341, 0.361)      # color of the tick labels
#xtick.labelsize      : medium # fontsize of the tick labels
xtick.labelsize      : 10 # fontsize of the tick labels
xtick.direction      : in     # direction: in, out, or inout

#ytick.major.size     : 4      # major tick size in points
#ytick.minor.size     : 2      # minor tick size in points
#ytick.major.width    : 0.5    # major tick width in points
ytick.major.width    : 0    # major tick width in points
#ytick.minor.width    : 0.5    # minor tick width in points
#ytick.major.pad      : 4      # distance to major tick label in points
#ytick.minor.pad      : 4      # distance to the minor tick label in points
ytick.color          : (0.322, 0.341, 0.361)      # color of the tick labels
#ytick.labelsize      : medium # fontsize of the tick labels
ytick.labelsize      : 10 # fontsize of the tick labels
ytick.direction      : in     # direction: in, out, or inout


### GRIDS
grid.color       :   2C3539   # grid color
grid.linestyle   :   :       # dotted
grid.linewidth   :   0.5      # in points
grid.alpha       :   1.0     # transparency, between 0.0 and 1.0

### Legend
#legend.fancybox      : False  # if True, use a rounded box for the
                               # legend, else a rectangle
#legend.isaxes        : True
#legend.numpoints     : 2      # the number of points in the legend line
legend.fontsize      : 8
#legend.borderpad     : 0.5    # border whitespace in fontsize units
legend.markerscale   : 0.8    # the relative size of legend markers vs. original
# the following dimensions are in axes coords
#legend.labelspacing  : 0.5    # the vertical space between the legend entries in fraction of fontsize
#legend.handlelength  : 2.     # the length of the legend lines in fraction of fontsize
#legend.handleheight  : 0.7     # the height of the legend handle in fraction of fontsize
#legend.handletextpad : 0.8    # the space between the legend line and legend text in fraction of fontsize
#legend.borderaxespad : 0.5   # the border between the axes and legend edge in fraction of fontsize
#legend.columnspacing : 2.    # the border between the axes and legend edge in fraction of fontsize
#legend.shadow        : False
legend.frameon       : False   # whether or not to draw a frame around legend
#legend.framealpha    : None    # opacity of of legend frame
#legend.scatterpoints : 3 # number of scatter points

### FIGURE
# See http://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure
#figure.titlesize : medium     # size of the figure title
#figure.titleweight : normal   # weight of the figure title
#figure.figsize   : 8, 6    # figure size in inches
figure.figsize   : 7, 5    # figure size in inches
figure.dpi       : 150      # figure dots per inch
#figure.facecolor : 0.75    # figure facecolor; 0.75 is scalar gray
#figure.edgecolor : white   # figure edgecolor
#figure.autolayout : False  # When True, automatically adjust subplot
                            # parameters to make the plot fit the figure
#figure.max_open_warning : 20  # The maximum number of figures to open through
                               # the pyplot interface before emitting a warning.
                               # If less than one this feature is disabled.

# The figure subplot parameters.  All dimensions are a fraction of the
# figure width or height
#figure.subplot.left    : 0.125  # the left side of the subplots of the figure
#figure.subplot.right   : 0.9    # the right side of the subplots of the figure
#figure.subplot.bottom  : 0.1    # the bottom of the subplots of the figure
#figure.subplot.top     : 0.9    # the top of the subplots of the figure
#figure.subplot.wspace  : 0.2    # the amount of width reserved for blank space between subplots
#figure.subplot.hspace  : 0.2    # the amount of height reserved for white space between subplots

### IMAGES
#image.aspect : equal             # equal | auto | a number
#image.interpolation  : bilinear  # see help(imshow) for options
#image.cmap   : jet               # gray | jet etc...
#image.lut    : 256               # the size of the colormap lookup table
#image.origin : upper             # lower | upper
#image.resample  : False
#image.composite_image : True     # When True, all the images on a set of axes are 
                                  # combined into a single composite image before 
                                  # saving a figure as a vector graphics file, 
                                  # such as a PDF.

### CONTOUR PLOTS
#contour.negative_linestyle : dashed # dashed | solid
#contour.corner_mask        : True   # True | False | legacy

### ERRORBAR PLOTS
#errorbar.capsize : 3             # length of end cap on error bars in pixels

### Agg rendering
### Warning: experimental, 2008/10/10
#agg.path.chunksize : 0           # 0 to disable; values in the range
                                  # 10000 to 100000 can improve speed slightly
                                  # and prevent an Agg rendering failure
                                  # when plotting very large data sets,
                                  # especially if they are very gappy.
                                  # It may cause minor artifacts, though.
                                  # A value of 20000 is probably a good
                                  # starting point.
### SAVING FIGURES
#path.simplify : True   # When True, simplify paths by removing "invisible"
                        # points to reduce file size and increase rendering
                        # speed
#path.simplify_threshold : 0.1  # The threshold of similarity below which
                                # vertices will be removed in the simplification
                                # process
#path.snap : True # When True, rectilinear axis-aligned paths will be snapped to
                  # the nearest pixel when certain criteria are met.  When False,
                  # paths will never be snapped.
#path.sketch : None # May be none, or a 3-tuple of the form (scale, length,
                    # randomness).
                    # *scale* is the amplitude of the wiggle
                    # perpendicular to the line (in pixels).  *length*
                    # is the length of the wiggle along the line (in
                    # pixels).  *randomness* is the factor by which
                    # the length is randomly scaled.

# the default savefig params can be different from the display params
# e.g., you may want a higher resolution, or to make the figure
# background white
savefig.dpi         : 100      # figure dots per inch
#savefig.facecolor   : white    # figure facecolor when saving
#savefig.edgecolor   : white    # figure edgecolor when saving
#savefig.format      : png      # png, ps, pdf, svg
#savefig.bbox        : standard # 'tight' or 'standard'.
                                # 'tight' is incompatible with pipe-based animation
                                # backends but will workd with temporary file based ones:
                                # e.g. setting animation.writer to ffmpeg will not work,
                                # use ffmpeg_file instead
savefig.bbox        : tight # 'tight' or 'standard'.
                                # 'tight' is incompatible with pipe-based animation
                                # backends but will workd with temporary file based ones:
                                # e.g. setting animation.writer to ffmpeg will not work,
                                # use ffmpeg_file instead
#savefig.pad_inches  : 0.1      # Padding to be used when bbox is set to 'tight'
#savefig.jpeg_quality: 95       # when a jpeg is saved, the default quality parameter.
#savefig.directory   : ~        # default directory in savefig dialog box,
                                # leave empty to always use current working directory
#savefig.transparent : False    # setting that controls whether figures are saved with a
                                # transparent background by default

# tk backend params
#tk.window_focus   : False    # Maintain shell focus for TkAgg

# ps backend params
#ps.papersize      : letter   # auto, letter, legal, ledger, A0-A10, B0-B10
#ps.useafm         : False    # use of afm fonts, results in small files
#ps.usedistiller   : False    # can be: None, ghostscript or xpdf
                                          # Experimental: may produce smaller files.
                                          # xpdf intended for production of publication quality files,
                                          # but requires ghostscript, xpdf and ps2eps
#ps.distiller.res  : 6000      # dpi
#ps.fonttype       : 3         # Output Type 3 (Type3) or Type 42 (TrueType)

# pdf backend params
#pdf.compression   : 6 # integer from 0 to 9
                       # 0 disables compression (good for debugging)
#pdf.fonttype       : 3         # Output Type 3 (Type3) or Type 42 (TrueType)

# svg backend params
#svg.image_inline : True       # write raster image data directly into the svg file
#svg.image_noscale : False     # suppress scaling of raster data embedded in SVG
#svg.fonttype : 'path'         # How to handle SVG fonts:
#    'none': Assume fonts are installed on the machine where the SVG will be viewed.
#    'path': Embed characters as paths -- supported by most SVG renderers
#    'svgfont': Embed characters as SVG fonts -- supported only by Chrome,
#               Opera and Safari

# docstring params
#docstring.hardcopy = False  # set this when you want to generate hardcopy docstring

# Set the verbose flags.  This controls how much information
# matplotlib gives you at runtime and where it goes.  The verbosity
# levels are: silent, helpful, debug, debug-annoying.  Any level is
# inclusive of all the levels below it.  If your setting is "debug",
# you'll get all the debug and helpful messages.  When submitting
# problems to the mailing-list, please set verbose to "helpful" or "debug"
# and paste the output into your report.
#
# The "fileo" gives the destination for any calls to verbose.report.
# These objects can a filename, or a filehandle like sys.stdout.
#
# You can override the rc default verbosity from the command line by
# giving the flags --verbose-LEVEL where LEVEL is one of the legal
# levels, e.g., --verbose-helpful.
#
# You can access the verbose instance in your code
#   from matplotlib import verbose.
#verbose.level  : silent      # one of silent, helpful, debug, debug-annoying
#verbose.fileo  : sys.stdout  # a log filename, sys.stdout or sys.stderr

# Event keys to interact with figures/plots via keyboard.
# Customize these settings according to your needs.
# Leave the field(s) empty if you don't need a key-map. (i.e., fullscreen : '')

#keymap.fullscreen : f               # toggling
#keymap.home : h, r, home            # home or reset mnemonic
#keymap.back : left, c, backspace    # forward / backward keys to enable
#keymap.forward : right, v           #   left handed quick navigation
#keymap.pan : p                      # pan mnemonic
#keymap.zoom : o                     # zoom mnemonic
#keymap.save : s                     # saving current figure
#keymap.quit : ctrl+w, cmd+w         # close the current figure
#keymap.grid : g                     # switching on/off a grid in current axes
#keymap.yscale : l                   # toggle scaling of y-axes ('log'/'linear')
#keymap.xscale : L, k                # toggle scaling of x-axes ('log'/'linear')
#keymap.all_axes : a                 # enable all axes

# Control location of examples data files
#examples.directory : ''   # directory to look in for custom installation

###ANIMATION settings
#animation.html : 'none'           # How to display the animation as HTML in
                                   # the IPython notebook. 'html5' uses
                                   # HTML5 video tag.
#animation.writer : ffmpeg         # MovieWriter 'backend' to use
#animation.codec : mpeg4           # Codec to use for writing movie
#animation.bitrate: -1             # Controls size/quality tradeoff for movie.
                                   # -1 implies let utility auto-determine
#animation.frame_format: 'png'     # Controls frame format used by temp files
#animation.ffmpeg_path: 'ffmpeg'   # Path to ffmpeg binary. Without full path
                                   # $PATH is searched
#animation.ffmpeg_args: ''         # Additional arguments to pass to ffmpeg
#animation.avconv_path: 'avconv'   # Path to avconv binary. Without full path
                                   # $PATH is searched
#animation.avconv_args: ''         # Additional arguments to pass to avconv
#animation.mencoder_path: 'mencoder'
                                   # Path to mencoder binary. Without full path
                                   # $PATH is searched
#animation.mencoder_args: ''       # Additional arguments to pass to mencoder
#animation.convert_path: 'convert' # Path to ImageMagick's convert binary.
                                   # On Windows use the full path since convert
                                   # is also the name of a system tool.