.. redirect-from:: /users/dflt_style_changes

============================== Changes to the default style

The most important changes in matplotlib 2.0 are the changes to the default style.

While it is impossible to select the best default for all cases, these are designed to work well in the most common cases.

A 'classic' style sheet is provided so reverting to the 1.x default values is a single line of python

.. code-block:: python

import matplotlib.style import matplotlib as mpl mpl.style.use('classic')

See :ref:customizing-with-matplotlibrc-files for details about how to persistently and selectively revert many of these changes.

.. contents:: Table of Contents :depth: 2 :local: :backlinks: entry

Colors, color cycles, and colormaps

Colors in default property cycle

The colors in the default property cycle have been changed from ['b', 'g', 'r', 'c', 'm', 'y', 'k'] to the category10 color palette used by Vega <https://github.com/vega/vega/wiki/Scales#scale-range-literals>__ and d3 <https://github.com/d3/d3-3.x-api-reference/blob/master/Ordinal-Scales.md#category10>__ originally developed at Tableau.

.. plot::

import numpy as np import matplotlib.pyplot as plt

th = np.linspace(0, 2*np.pi, 512)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))

def color_demo(ax, colors, title): ax.set_title(title) for j, c in enumerate(colors): v_offset = -(j / len(colors)) ax.plot(th, .1*np.sin(th) + v_offset, color=c) ax.annotate("'C{}'".format(j), (0, v_offset), xytext=(-1.5, 0), ha='right', va='center', color=c, textcoords='offset points', family='monospace')

      ax.annotate("{!r}".format(c), (2*np.pi, v_offset),
                  xytext=(1.5, 0),
                  ha='left',
                  va='center',
                  color=c,
                  textcoords='offset points',
                  family='monospace')
  ax.axis('off')

old_colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']

new_colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']

color_demo(ax1, old_colors, 'classic') color_demo(ax2, new_colors, 'v2.0')

fig.subplots_adjust(**{'bottom': 0.0, 'left': 0.059, 'right': 0.869, 'top': 0.895})

In addition to changing the colors, an additional method to specify colors was added. Previously, the default colors were the single character short-hand notations for red, green, blue, cyan, magenta, yellow, and black. This made them easy to type and usable in the abbreviated style string in plot, however the new default colors are only specified via hex values. To access these colors outside of the property cycling the notation for colors 'CN', where N takes values 0-9, was added to denote the first 10 colors in :rc:axes.prop_cycle. See :ref:colors_def for more details.

To restore the old color cycle use

.. code-block:: python

from cycler import cycler mpl.rcParams['axes.prop_cycle'] = cycler(color='bgrcmyk')

or set

.. code-block:: cfg

axes.prop_cycle : cycler('color', 'bgrcmyk')

in your :file:matplotlibrc file.

Colormap

The new default colormap used by matplotlib.cm.ScalarMappable instances is 'viridis' (aka option D <https://bids.github.io/colormap/>__).

.. plot::

import numpy as np import matplotlib.pyplot as plt

N = M = 200 X, Y = np.ogrid[0:20:N1j, 0:20:M1j] data = np.sin(np.pi * X2 / 20) * np.cos(np.pi * Y2 / 20)

fig, (ax2, ax1) = plt.subplots(1, 2, figsize=(7, 3)) im = ax1.imshow(data, extent=[0, 200, 0, 200]) ax1.set_title("v2.0: 'viridis'") fig.colorbar(im, ax=ax1, shrink=0.8)

im2 = ax2.imshow(data, extent=[0, 200, 0, 200], cmap='jet') fig.colorbar(im2, ax=ax2, shrink=0.8) ax2.set_title("classic: 'jet'")

fig.tight_layout()

For an introduction to color theory and how 'viridis' was generated watch Nathaniel Smith and Stéfan van der Walt's talk from SciPy2015. See here for many more details <https://bids.github.io/colormap/>__ about the other alternatives and the tools used to create the color map. For details on all of the colormaps available in matplotlib see :ref:colormaps.

.. raw:: html

<iframe width="560" height="315" src="https://www.youtube.com/embed/xAoljeRJ3lU" frameborder="0" allowfullscreen></iframe>

The previous default can be restored using

.. code-block:: python

mpl.rcParams['image.cmap'] = 'jet'

or setting

.. code-block:: cfg

image.cmap : 'jet'

in your :file:matplotlibrc file; however this is strongly discouraged.

Interactive figures

The default interactive figure background color has changed from grey to white, which matches the default background color used when saving.

The previous defaults can be restored by ::

mpl.rcParams['figure.facecolor'] = '0.75'

or by setting ::

figure.facecolor : '0.75'

in your :file:matplotlibrc file.

Grid lines

The default style of grid lines was changed from black dashed lines to thicker solid light grey lines.

.. plot::

import numpy as np import matplotlib.pyplot as plt

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))

ax1.grid(color='k', linewidth=.5, linestyle=':') ax1.set_title('classic')

ax2.grid() ax2.set_title('v2.0')

The previous default can be restored by using::

mpl.rcParams['grid.color'] = 'k' mpl.rcParams['grid.linestyle'] = ':' mpl.rcParams['grid.linewidth'] = 0.5

or by setting::

grid.color : k # grid color grid.linestyle : : # dotted grid.linewidth : 0.5 # in points

in your :file:matplotlibrc file.

Figure size, font size, and screen dpi

The default dpi used for on-screen display was changed from 80 dpi to 100 dpi, the same as the default dpi for saving files. Due to this change, the on-screen display is now more what-you-see-is-what-you-get for saved files. To keep the figure the same size in terms of pixels, in order to maintain approximately the same size on the screen, the default figure size was reduced from 8x6 inches to 6.4x4.8 inches. As a consequence of this the default font sizes used for the title, tick labels, and axes labels were reduced to maintain their size relative to the overall size of the figure. By default the dpi of the saved image is now the dpi of the ~matplotlib.figure.Figure instance being saved.

This will have consequences if you are trying to match text in a figure directly with external text.

The previous defaults can be restored by ::

mpl.rcParams['figure.figsize'] = [8.0, 6.0] mpl.rcParams['figure.dpi'] = 80 mpl.rcParams['savefig.dpi'] = 100

mpl.rcParams['font.size'] = 12 mpl.rcParams['legend.fontsize'] = 'large' mpl.rcParams['figure.titlesize'] = 'medium'

or by setting::

figure.figsize : [8.0, 6.0] figure.dpi : 80 savefig.dpi : 100

font.size : 12.0 legend.fontsize : 'large' figure.titlesize : 'medium'

In your :file:matplotlibrc file.

In addition, the forward kwarg to ~.Figure.set_size_inches now defaults to True to improve the interactive experience. Backend canvases that adjust the size of their bound matplotlib.figure.Figure must pass forward=False to avoid circular behavior. This default is not configurable.

Plotting functions

`scatter`

The following changes were made to the default behavior of ~matplotlib.axes.Axes.scatter

The default size of the elements in a scatter plot is now based on :rc:lines.markersize so it is consistent with plot(X, Y, 'o'). The old value was 20, and the new value is 36 (6^2).
Scatter markers no longer have a black edge.
If the color of the markers is not specified it will follow the property cycle, pulling from the 'patches' cycle on the Axes.

.. plot::

import numpy as np import matplotlib.pyplot as plt

np.random.seed(2)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))

x = np.arange(15) y = np.random.rand(15) y2 = np.random.rand(15) ax1.scatter(x, y, s=20, edgecolors='k', c='b', label='a') ax1.scatter(x, y2, s=20, edgecolors='k', c='b', label='b') ax1.legend() ax1.set_title('classic')

ax2.scatter(x, y, label='a') ax2.scatter(x, y2, label='b') ax2.legend() ax2.set_title('v2.0')

The classic default behavior of ~matplotlib.axes.Axes.scatter can only be recovered through mpl.style.use('classic'). The marker size can be recovered via ::

mpl.rcParam['lines.markersize'] = np.sqrt(20)

however, this will also affect the default marker size of ~matplotlib.axes.Axes.plot. To recover the classic behavior on a per-call basis pass the following kwargs::

classic_kwargs = {'s': 20, 'edgecolors': 'k', 'c': 'b'}

`plot`

The following changes were made to the default behavior of ~matplotlib.axes.Axes.plot

the default linewidth increased from 1 to 1.5
the dash patterns associated with '--', ':', and '-.' have changed
the dash patterns now scale with line width

.. plot::

import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl from cycler import cycler

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))

N = 15

x = np.arange(N) y = np.ones_like(x)

sty_cycle = (cycler('ls', ['--' ,':', '-.']) * cycler('lw', [None, 1, 2, 5]))

classic = { 'lines.linewidth': 1.0, 'lines.dashed_pattern' : [6, 6], 'lines.dashdot_pattern' : [3, 5, 1, 5], 'lines.dotted_pattern' : [1, 3], 'lines.scale_dashes': False}

v2 = {}

{'lines.linewidth': 1.5,

'lines.dashed_pattern' : [2.8, 1.2],

'lines.dashdot_pattern' : [4.8, 1.2, 0.8, 1.2],

'lines.dotted_pattern' : [1.1, 1.1],

'lines.scale_dashes': True}

def demo(ax, rcparams, title): ax.axis('off') ax.set_title(title) with mpl.rc_context(rc=rcparams): for j, sty in enumerate(sty_cycle): ax.plot(x, y + j, **sty)

demo(ax1, classic, 'classic') demo(ax2, {}, 'v2.0')

The previous defaults can be restored by setting::

mpl.rcParams['lines.linewidth'] = 1.0
mpl.rcParams['lines.dashed_pattern'] = [6, 6]
mpl.rcParams['lines.dashdot_pattern'] = [3, 5, 1, 5]
mpl.rcParams['lines.dotted_pattern'] = [1, 3]
mpl.rcParams['lines.scale_dashes'] = False

or by setting::

lines.linewidth : 1.0 lines.dashed_pattern : 6, 6 lines.dashdot_pattern : 3, 5, 1, 5 lines.dotted_pattern : 1, 3 lines.scale_dashes: False

in your :file:matplotlibrc file.

`errorbar`

By default, caps on the ends of errorbars are not present.

.. plot::

import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np

example data

x = np.arange(0.1, 4, 0.5) y = np.exp(-x)

example variable error bar values

yerr = 0.1 + 0.2*np.sqrt(x) xerr = 0.1 + yerr

def demo(ax, rc, title): with mpl.rc_context(rc=rc): ax.errorbar(x, y, xerr=0.2, yerr=0.4) ax.set_title(title)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), tight_layout=True)

demo(ax1, {'errorbar.capsize': 3}, 'classic') demo(ax2, {}, 'v2.0')

This also changes the return value of :meth:~matplotlib.axes.Axes.errorbar as the list of 'caplines' will be empty by default.

The previous defaults can be restored by setting::

mpl.rcParams['errorbar.capsize'] = 3

or by setting ::

errorbar.capsize : 3

in your :file:matplotlibrc file.

`boxplot`

Previously, boxplots were composed of a mish-mash of styles that were, for better for worse, inherited from Matlab. Most of the elements were blue, but the medians were red. The fliers (outliers) were black plus-symbols ('+') and the whiskers were dashed lines, which created ambiguity if the (solid and black) caps were not drawn.

For the new defaults, everything is black except for the median and mean lines (if drawn), which are set to the first two elements of the current color cycle. Also, the default flier markers are now hollow circles, which maintain the ability of the plus-symbols to overlap without obscuring data too much.

.. plot::

import numpy as np
import matplotlib.pyplot as plt

data = np.random.lognormal(size=(37, 4))
fig, (old, new) = plt.subplots(ncols=2, sharey=True)
with plt.style.context('default'):
    new.boxplot(data, labels=['A', 'B', 'C', 'D'])
    new.set_title('v2.0')

with plt.style.context('classic'):
    old.boxplot(data, labels=['A', 'B', 'C', 'D'])
    old.set_title('classic')

new.set_yscale('log')
old.set_yscale('log')

The previous defaults can be restored by setting::

mpl.rcParams['boxplot.flierprops.color'] = 'k'
mpl.rcParams['boxplot.flierprops.marker'] = '+'
mpl.rcParams['boxplot.flierprops.markerfacecolor'] = 'none'
mpl.rcParams['boxplot.flierprops.markeredgecolor'] = 'k'
mpl.rcParams['boxplot.boxprops.color'] = 'b'
mpl.rcParams['boxplot.whiskerprops.color'] = 'b'
mpl.rcParams['boxplot.whiskerprops.linestyle'] = '--'
mpl.rcParams['boxplot.medianprops.color'] = 'r'
mpl.rcParams['boxplot.meanprops.color'] = 'r'
mpl.rcParams['boxplot.meanprops.marker'] = '^'
mpl.rcParams['boxplot.meanprops.markerfacecolor'] = 'r'
mpl.rcParams['boxplot.meanprops.markeredgecolor'] = 'k'
mpl.rcParams['boxplot.meanprops.markersize'] = 6
mpl.rcParams['boxplot.meanprops.linestyle'] = '--'
mpl.rcParams['boxplot.meanprops.linewidth'] = 1.0

or by setting::

boxplot.flierprops.color:           'k'
boxplot.flierprops.marker:          '+'
boxplot.flierprops.markerfacecolor: 'none'
boxplot.flierprops.markeredgecolor: 'k'
boxplot.boxprops.color:             'b'
boxplot.whiskerprops.color:         'b'
boxplot.whiskerprops.linestyle:     '--'
boxplot.medianprops.color:          'r'
boxplot.meanprops.color:            'r'
boxplot.meanprops.marker:           '^'
boxplot.meanprops.markerfacecolor:  'r'
boxplot.meanprops.markeredgecolor:  'k'
boxplot.meanprops.markersize:        6
boxplot.meanprops.linestyle:         '--'
boxplot.meanprops.linewidth:         1.0

in your :file:matplotlibrc file.

`fill_between` and `fill_betweenx`

~matplotlib.axes.Axes.fill_between and ~matplotlib.axes.Axes.fill_betweenx both follow the patch color cycle.

.. plot::

import matplotlib.pyplot as plt import numpy as np

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3)) fig.subplots_adjust(wspace=0.3) th = np.linspace(0, 2*np.pi, 128) N = 5

def demo(ax, extra_kwargs, title): ax.set_title(title) return [ax.fill_between(th, np.sin((j / N) * np.pi + th), alpha=.5, **extra_kwargs) for j in range(N)]

demo(ax1, {'facecolor': 'C0'}, 'classic') demo(ax2, {}, 'v2.0')

If the facecolor is set via the facecolors or color keyword argument, then the color is not cycled.

To restore the previous behavior, explicitly pass the keyword argument facecolors='C0' to the method call.

Patch edges and color

Most artists drawn with a patch (~matplotlib.axes.Axes.bar, ~matplotlib.axes.Axes.pie, etc) no longer have a black edge by default. The default face color is now 'C0' instead of 'b'.

.. plot::

import matplotlib.pyplot as plt import numpy as np from matplotlib import rc_context import matplotlib.patches as mpatches

fig, all_ax = plt.subplots(3, 2, figsize=(4, 6), tight_layout=True)

def demo(ax_top, ax_mid, ax_bottom, rcparams, label): labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' fracs = [15, 30, 45, 10]

   explode = (0, 0.05, 0, 0)

   ax_top.set_title(label)

   with rc_context(rc=rcparams):
       ax_top.pie(fracs, labels=labels)
       ax_top.set_aspect('equal')
       ax_mid.bar(range(len(fracs)), fracs, tick_label=labels)
       plt.setp(ax_mid.get_xticklabels(), rotation=-45)
       grid = np.mgrid[0.2:0.8:3j, 0.2:0.8:3j].reshape(2, -1).T

       ax_bottom.set_xlim(0, .75)
       ax_bottom.set_ylim(0, .75)
       ax_bottom.add_artist(mpatches.Rectangle(grid[1] - [0.025, 0.05],
                                               0.05, 0.1))
       ax_bottom.add_artist(mpatches.RegularPolygon(grid[3], 5, radius=0.1))
       ax_bottom.add_artist(mpatches.Ellipse(grid[4], 0.2, 0.1))
       ax_bottom.add_artist(mpatches.Circle(grid[0], 0.1))
       ax_bottom.axis('off')

demo(*all_ax[:, 0], rcparams={'patch.force_edgecolor': True, 'patch.facecolor': 'b'}, label='classic') demo(*all_ax[:, 1], rcparams={}, label='v2.0')

The previous defaults can be restored by setting::

mpl.rcParams['patch.force_edgecolor'] = True
mpl.rcParams['patch.facecolor'] = 'b'

or by setting::

patch.facecolor : b patch.force_edgecolor : True

in your :file:matplotlibrc file.

`hexbin`

The default value of the linecolor keyword argument for ~.Axes.hexbin has changed from 'none' to 'face'. If 'none' is now supplied, no line edges are drawn around the hexagons.

.. _barbarh_align:

`bar` and `barh`

The default value of the align kwarg for both ~.Axes.bar and ~.Axes.barh is changed from 'edge' to 'center'.

.. plot::

import matplotlib.pyplot as plt import numpy as np

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(5, 5))

def demo(bar_func, bar_kwargs): return bar_func([1, 2, 3], [1, 2, 3], tick_label=['a', 'b', 'c'], **bar_kwargs)

ax1.set_title("classic") ax2.set_title('v2.0')

demo(ax1.bar, {'align': 'edge'}) demo(ax2.bar, {}) demo(ax3.barh, {'align': 'edge'}) demo(ax4.barh, {})

To restore the previous behavior explicitly pass the keyword argument align='edge' to the method call.

Hatching

The color of the lines in the hatch is now determined by

If an edge color is explicitly set, use that for the hatch color
If the edge color is not explicitly set, use :rc:hatch.color which is looked up at artist creation time.

The width of the lines in a hatch pattern is now configurable by the rcParams :rc:hatch.linewidth, which defaults to 1 point. The old behavior for the line width was different depending on backend:

PDF: 0.1 pt
SVG: 1.0 pt
PS: 1 px
Agg: 1 px

The old line width behavior cannot be restored across all backends simultaneously, but can be restored for a single backend by setting::

mpl.rcParams['hatch.linewidth'] = 0.1 # previous pdf hatch linewidth mpl.rcParams['hatch.linewidth'] = 1.0 # previous svg hatch linewidth

The behavior of the PS and Agg backends was DPI dependent, thus::

mpl.rcParams['figure.dpi'] = dpi mpl.rcParams['savefig.dpi'] = dpi # or leave as default 'figure' mpl.rcParams['hatch.linewidth'] = 1.0 / dpi # previous ps and Agg hatch linewidth

There is no direct API level control of the hatch color or linewidth.

Hatching patterns are now rendered at a consistent density, regardless of DPI. Formerly, high DPI figures would be more dense than the default, and low DPI figures would be less dense. This old behavior cannot be directly restored, but the density may be increased by repeating the hatch specifier.

.. _default_changes_font:

Fonts

Normal text

The default font has changed from "Bitstream Vera Sans" to "DejaVu Sans". DejaVu Sans has additional international and math characters, but otherwise has the same appearance as Bitstream Vera Sans. Latin, Greek, Cyrillic, Armenian, Georgian, Hebrew, and Arabic are all supported <https://dejavu-fonts.github.io/>__ (but right-to-left rendering is still not handled by matplotlib). In addition, DejaVu contains a sub-set of emoji symbols.

.. plot::

from future import unicode_literals

import matplotlib.pyplot as plt

fig, ax = plt.subplots() tick_labels = ['😃', '😎', '😴', '😲', '😻'] bar_labels = ['א', 'α', '☣', '⌬', 'ℝ'] y = [1, 4, 9, 16, 25] x = range(5) ax.bar(x, y, tick_label=tick_labels, align='center') ax.xaxis.set_tick_params(labelsize=20) for _x, _y, t in zip(x, y, bar_labels): ax.annotate(t, (_x, _y), fontsize=20, ha='center', xytext=(0, -2), textcoords='offset pixels', bbox={'facecolor': 'w'})

ax.set_title('Диаграмма со смайликами')

See the DejaVu Sans PDF sample for full coverage <http://dejavu.sourceforge.net/samples/DejaVuSans.pdf>__.

Math text

The default math font when using the built-in math rendering engine (mathtext) has changed from "Computer Modern" (i.e. LaTeX-like) to "DejaVu Sans". This change has no effect if the TeX backend is used (i.e. text.usetex is True).

.. plot::

import matplotlib.pyplot as plt import matplotlib as mpl

mpl.rcParams['mathtext.fontset'] = 'cm' mpl.rcParams['mathtext.rm'] = 'serif'

fig, ax = plt.subplots(tight_layout=True, figsize=(3, 3))

ax.plot(range(15), label=r'int: $15 \int_0^\infty dx$') ax.legend() ax.set_title('classic')

.. plot::

import matplotlib.pyplot as plt import matplotlib as mpl

fig, ax = plt.subplots(tight_layout=True, figsize=(3, 3))

ax.plot(range(15), label=r'int: $15 \int_0^\infty dx$') ax.legend() ax.set_title('v2.0')

To revert to the old behavior set the::

mpl.rcParams['mathtext.fontset'] = 'cm' mpl.rcParams['mathtext.rm'] = 'serif'

or set::

mathtext.fontset: cm mathtext.rm : serif

in your :file:matplotlibrc file.

This rcParam is consulted when the text is drawn, not when the artist is created. Thus all mathtext on a given canvas will use the same fontset.

Legends

By default, the number of points displayed in a legend is now 1.
The default legend location is 'best', so the legend will be automatically placed in a location to minimize overlap with data.
The legend defaults now include rounded corners, a lighter boundary, and partially transparent boundary and background.

.. plot::

import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np

def demo(ax, rcparams, title): np.random.seed(2) N = 25 with mpl.rc_context(rc=rcparams): x = range(N) y = np.cumsum(np.random.randn(N) ) # unpack the single Line2D artist ln, = ax.plot(x, y, marker='s', linestyle='-', label='plot') ax.fill_between(x, y, 0, label='fill', alpha=.5, color=ln.get_color()) ax.scatter(N*np.random.rand(N), np.random.rand(N), label='scatter') ax.set_title(title) ax.legend()

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), tight_layout=True)

classic_rc = {'legend.fancybox': False, 'legend.numpoints': 2, 'legend.scatterpoints': 3, 'legend.framealpha': None, 'legend.edgecolor': 'inherit', 'legend.loc': 'upper right', 'legend.fontsize': 'large'}

demo(ax1, classic_rc, 'classic') demo(ax2, {}, 'v2.0')

The previous defaults can be restored by setting::

mpl.rcParams['legend.fancybox'] = False mpl.rcParams['legend.loc'] = 'upper right' mpl.rcParams['legend.numpoints'] = 2 mpl.rcParams['legend.fontsize'] = 'large' mpl.rcParams['legend.framealpha'] = None mpl.rcParams['legend.scatterpoints'] = 3 mpl.rcParams['legend.edgecolor'] = 'inherit'

or by setting::

legend.fancybox : False legend.loc : upper right legend.numpoints : 2 # the number of points in the legend line legend.fontsize : large legend.framealpha : None # opacity of legend frame legend.scatterpoints : 3 # number of scatter points legend.edgecolor : inherit # legend edge color ('inherit' # means it uses axes.edgecolor)

in your :file:matplotlibrc file.

Image

Interpolation

The default interpolation method for ~matplotlib.axes.Axes.imshow is now 'nearest' and by default it resamples the data (both up and down sampling) before colormapping.

.. plot::

import matplotlib.pyplot as plt import matplotlib as mpl import numpy as np

def demo(ax, rcparams, title): np.random.seed(2) A = np.random.rand(5, 5)

   with mpl.rc_context(rc=rcparams):
       ax.imshow(A)
       ax.set_title(title)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), tight_layout=True)

classic_rcparams = {'image.interpolation': 'bilinear', 'image.resample': False}

demo(ax1, classic_rcparams, 'classic') demo(ax2, {}, 'v2.0')

To restore the previous behavior set::

mpl.rcParams['image.interpolation'] = 'bilinear' mpl.rcParams['image.resample'] = False

or set::

image.interpolation : bilinear # see help(imshow) for options image.resample : False

in your :file:matplotlibrc file.

Colormapping pipeline

Previously, the input data was normalized, then colormapped, and then resampled to the resolution required for the screen. This meant that the final resampling was being done in color space. Because the color maps are not generally linear in RGB space, colors not in the colormap may appear in the final image. This bug was addressed by an almost complete overhaul of the image handling code.

The input data is now normalized, then resampled to the correct resolution (in normalized dataspace), and then colormapped to RGB space. This ensures that only colors from the colormap appear in the final image. (If your viewer subsequently resamples the image, the artifact may reappear.)

The previous behavior cannot be restored.

Shading

The default shading mode for light source shading, in matplotlib.colors.LightSource.shade, is now overlay. Formerly, it was hsv.

Plot layout

Auto limits

The previous auto-scaling behavior was to find 'nice' round numbers as view limits that enclosed the data limits, but this could produce bad plots if the data happened to fall on a vertical or horizontal line near the chosen 'round number' limit. The new default sets the view limits to 5% wider than the data range.

.. plot::

import matplotlib as mpl import matplotlib.pyplot as plt import numpy

data = np.zeros(1000) data[0] = 1

fig = plt.figure(figsize=(6, 3))

def demo(fig, rc, title, j): with mpl.rc_context(rc=rc): ax = fig.add_subplot(1, 2, j) ax.plot(data) ax.set_title(title)

demo(fig, {'axes.autolimit_mode': 'round_numbers', 'axes.xmargin': 0, 'axes.ymargin': 0}, 'classic', 1) demo(fig, {}, 'v2.0', 2)

The size of the padding in the x and y directions is controlled by the 'axes.xmargin' and 'axes.ymargin' rcParams respectively. Whether the view limits should be 'round numbers' is controlled by :rc:axes.autolimit_mode. In the original 'round_number' mode, the view limits coincide with ticks.

The previous default can be restored by using::

mpl.rcParams['axes.autolimit_mode'] = 'round_numbers' mpl.rcParams['axes.xmargin'] = 0 mpl.rcParams['axes.ymargin'] = 0

or setting::

axes.autolimit_mode: round_numbers axes.xmargin: 0 axes.ymargin: 0

in your :file:matplotlibrc file.

Z-order

Ticks and grids are now plotted above solid elements such as filled contours, but below lines. To return to the previous behavior of plotting ticks and grids above lines, set rcParams['axes.axisbelow'] = False.

Ticks

Direction


To reduce the collision of tick marks with data, the default ticks now
point outward by default.  In addition, ticks are now drawn only on
the bottom and left spines to prevent a porcupine appearance, and for
a cleaner separation between subplots.


.. plot::

   import matplotlib as mpl
   import matplotlib.pyplot as plt
   import numpy as np

   th = np.linspace(0, 2*np.pi, 128)
   y = np.sin(th)

   def demo(fig, rcparams, title, j):
       np.random.seed(2)
       with mpl.rc_context(rc=rcparams):

           ax = fig.add_subplot(2, 2, j)
           ax.hist(np.random.beta(0.5, 0.5, 10000), 25, density=True)
           ax.set_xlim([0, 1])
           ax.set_title(title)

           ax = fig.add_subplot(2, 2, j + 2)
           ax.imshow(np.random.rand(5, 5))

   classic = {'xtick.direction': 'in',
              'ytick.direction': 'in',
              'xtick.top': True,
              'ytick.right': True}

   fig = plt.figure(figsize=(6, 6), tight_layout=True)

   demo(fig, classic, 'classic', 1)
   demo(fig, {}, 'v2.0', 2)


To restore the previous behavior set::

   mpl.rcParams['xtick.direction'] = 'in'
   mpl.rcParams['ytick.direction'] = 'in'
   mpl.rcParams['xtick.top'] = True
   mpl.rcParams['ytick.right'] = True

or set::

   xtick.top: True
   xtick.direction: in

   ytick.right: True
   ytick.direction: in

in your :file:`matplotlibrc` file.



Number of ticks

The default ~matplotlib.ticker.Locator used for the x and y axis is ~matplotlib.ticker.AutoLocator which tries to find, up to some maximum number, 'nicely' spaced ticks. The locator now includes an algorithm to estimate the maximum number of ticks that will leave room for the tick labels. By default it also ensures that there are at least two ticks visible.

.. plot::

import matplotlib.pyplot as plt import numpy as np

from matplotlib.ticker import AutoLocator

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(4, 3), tight_layout=True) ax1.set_xlim(0, .1) ax2.set_xlim(0, .1)

ax1.xaxis.get_major_locator().set_params(nbins=9, steps=[1, 2, 5, 10]) ax1.set_title('classic') ax2.set_title('v2.0')

There is no way, other than using mpl.style.use('classic'), to restore the previous behavior as the default. On an axis-by-axis basis you may either control the existing locator via: ::

ax.xaxis.get_major_locator().set_params(nbins=9, steps=[1, 2, 5, 10])

or create a new ~matplotlib.ticker.MaxNLocator::

import matplotlib.ticker as mticker ax.set_major_locator(mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10])

The algorithm used by ~matplotlib.ticker.MaxNLocator has been improved, and this may change the choice of tick locations in some cases. This also affects ~matplotlib.ticker.AutoLocator, which uses MaxNLocator internally.

For a log-scaled axis the default locator is the ~matplotlib.ticker.LogLocator. Previously the maximum number of ticks was set to 15, and could not be changed. Now there is a numticks kwarg for setting the maximum to any integer value, to the string 'auto', or to its default value of None which is equivalent to 'auto'. With the 'auto' setting the maximum number will be no larger than 9, and will be reduced depending on the length of the axis in units of the tick font size. As in the case of the AutoLocator, the heuristic algorithm reduces the incidence of overlapping tick labels but does not prevent it.

Tick label formatting

LogFormatter labeling of minor ticks


Minor ticks on a log axis are now labeled when the axis view limits
span a range less than or equal to the interval between two major
ticks.  See `~matplotlib.ticker.LogFormatter` for details. The
minor tick labeling is turned off when using ``mpl.style.use('classic')``,
but cannot be controlled independently via `.rcParams`.

.. plot::

   import numpy as np
   import matplotlib.pyplot as plt

   np.random.seed(2)

   fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(6, 3))
   fig.subplots_adjust(wspace=0.35, left=0.09, right=0.95)

   x = np.linspace(0.9, 1.7, 10)
   y = 10 ** x[np.random.randint(0, 10, 10)]

   ax2.semilogy(x, y)
   ax2.set_title('v2.0')

   with plt.style.context('classic'):
       ax1.semilogy(x, y)
       ax1.set_xlim(ax2.get_xlim())
       ax1.set_ylim(ax2.get_ylim())
       ax1.set_title('classic')


``ScalarFormatter`` tick label formatting with offsets

With the default :rc:axes.formatter.useoffset, an offset will be used when it will save 4 or more digits. This can be controlled with the new :rc:axes.formatter.offset_threshold. To restore the previous behavior of using an offset to save 2 or more digits, use rcParams['axes.formatter.offset_threshold'] = 2.

.. plot::

import numpy as np import matplotlib.pyplot as plt

np.random.seed(5)

fig = plt.figure(figsize=(6, 3)) fig.subplots_adjust(bottom=0.15, wspace=0.3, left=0.09, right=0.95)

x = np.linspace(2000, 2008, 9) y = np.random.randn(9) + 50000

with plt.rc_context(rc={'axes.formatter.offset_threshold' : 2}): ax1 = fig.add_subplot(1, 2, 1) ax1.plot(x, y) ax1.set_title('classic')

ax2 = fig.add_subplot(1, 2, 2) ax2.plot(x, y) ax2.set_title('v2.0')

AutoDateFormatter format strings


The default date formats are now all based on ISO format, i.e., with
the slowest-moving value first.  The date formatters are
configurable through the ``date.autoformatter.*`` rcParams.


+--------------------------------------+--------------------------------------+-------------------+-------------------+
| Threshold (tick interval >= than)    | rcParam                              | classic           | v2.0              |
+======================================+======================================+===================+===================+
| 365 days                             | ``'date.autoformatter.year'``        | ``'%Y'``          | ``'%Y'``          |
+--------------------------------------+--------------------------------------+-------------------+-------------------+
| 30 days                              | ``'date.autoformatter.month'``       | ``'%b %Y'``       | ``'%Y-%m'``       |
+--------------------------------------+--------------------------------------+-------------------+-------------------+
| 1 day                                | ``'date.autoformatter.day'``         | ``'%b %d %Y'``    | ``'%Y-%m-%d'``    |
+--------------------------------------+--------------------------------------+-------------------+-------------------+
| 1 hour                               | ``'date.autoformatter.hour'``        | ``'%H:%M:%S'``    | ``'%H:%M'``       |
+--------------------------------------+--------------------------------------+-------------------+-------------------+
| 1 minute                             | ``'date.autoformatter.minute'``      | ``'%H:%M:%S.%f'`` | ``'%H:%M:%S'``    |
+--------------------------------------+--------------------------------------+-------------------+-------------------+
| 1 second                             | ``'date.autoformatter.second'``      | ``'%H:%M:%S.%f'`` | ``'%H:%M:%S'``    |
+--------------------------------------+--------------------------------------+-------------------+-------------------+
| 1  microsecond                       | ``'date.autoformatter.microsecond'`` | ``'%H:%M:%S.%f'`` | ``'%H:%M:%S.%f'`` |
+--------------------------------------+--------------------------------------+-------------------+-------------------+



Python's ``%x`` and ``%X`` date formats may be of particular interest
to format dates based on the current locale.

The previous default can be restored by::

   mpl.rcParams['date.autoformatter.year'] = '%Y'
   mpl.rcParams['date.autoformatter.month'] = '%b %Y'
   mpl.rcParams['date.autoformatter.day'] = '%b %d %Y'
   mpl.rcParams['date.autoformatter.hour'] = '%H:%M:%S'
   mpl.rcParams['date.autoformatter.minute'] = '%H:%M:%S.%f'
   mpl.rcParams['date.autoformatter.second'] = '%H:%M:%S.%f'
   mpl.rcParams['date.autoformatter.microsecond'] = '%H:%M:%S.%f'


or setting ::

   date.autoformatter.year   : %Y
   date.autoformatter.month  : %b %Y
   date.autoformatter.day    : %b %d %Y
   date.autoformatter.hour   : %H:%M:%S
   date.autoformatter.minute : %H:%M:%S.%f
   date.autoformatter.second : %H:%M:%S.%f
   date.autoformatter.microsecond : %H:%M:%S.%f

in your :file:`matplotlibrc` file.

mplot3d
=======

- mplot3d now obeys some style-related rcParams, rather than using
  hard-coded defaults.  These include:

  - xtick.major.width
  - ytick.major.width
  - xtick.color
  - ytick.color
  - axes.linewidth
  - axes.edgecolor
  - grid.color
  - grid.linewidth
  - grid.linestyle

Changes to the default style

============================== Changes to the default style

Colors, color cycles, and colormaps

Colors in default property cycle

Colormap

Interactive figures

Grid lines

Figure size, font size, and screen dpi

Plotting functions

scatter

plot

{'lines.linewidth': 1.5,

'lines.dashed_pattern' : [2.8, 1.2],

'lines.dashdot_pattern' : [4.8, 1.2, 0.8, 1.2],

'lines.dotted_pattern' : [1.1, 1.1],

'lines.scale_dashes': True}

errorbar

example data

example variable error bar values

boxplot

fill_between and fill_betweenx

Patch edges and color

hexbin

bar and barh

Hatching

Fonts

Normal text

Math text

Legends

Image

Interpolation

Colormapping pipeline

Shading

Plot layout

Auto limits

Z-order

Ticks

Tick label formatting

`scatter`

`plot`

`errorbar`

`boxplot`

`fill_between` and `fill_betweenx`

`hexbin`

`bar` and `barh`