### We're updating the default styles for Matplotlib 2.0

Learn what to expect in the new updates

Travis-CI:

# AXISARTIST namespace¶

The AxisArtist namespace includes a derived Axes implementation. The biggest difference is that the artists responsible to draw axis line, ticks, ticklabel and axis labels are separated out from the mpl’s Axis class, which are much more than artists in the original mpl. This change was strongly motivated to support curvilinear grid. Here are a few things that mpl_tootlkits.axisartist.Axes is different from original Axes from mpl.

• Axis elements (axis line(spine), ticks, ticklabel and axis labels) are drawn by a AxisArtist instance. Unlike Axis, left, right, top and bottom axis are drawn by separate artists. And each of them may have different tick location and different tick labels.
• gridlines are drawn by a Gridlines instance. The change was motivated that in curvilinear coordinate, a gridline may not cross axis-lines (i.e., no associated ticks). In the original Axes class, gridlines are tied to ticks.
• ticklines can be rotated if necessary (i.e, along the gridlines)

In summary, all these changes was to support

• a curvilinear grid.
• a floating axis

mpl_toolkits.axisartist.Axes class defines a axis attribute, which is a dictionary of AxisArtist instances. By default, the dictionary has 4 AxisArtist instances, responsible for drawing of left, right, bottom and top axis.

xaxis and yaxis attributes are still available, however they are set to not visible. As separate artists are used for rendering axis, some axis-related method in mpl may have no effect. In addition to AxisArtist instances, the mpl_toolkits.axisartist.Axes will have gridlines attribute (Gridlines), which obviously draws grid lines.

In both AxisArtist and Gridlines, the calculation of tick and grid location is delegated to an instance of GridHelper class. mpl_toolkits.axisartist.Axes class uses GridHelperRectlinear as a grid helper. The GridHelperRectlinear class is a wrapper around the xaxis and yaxis of mpl’s original Axes, and it was meant to work as the way how mpl’s original axes works. For example, tick location changes using set_ticks method and etc. should work as expected. But change in artist properties (e.g., color) will not work in general, although some effort has been made so that some often-change attributes (color, etc.) are respected.

## AxisArtist¶

AxisArtist can be considered as a container artist with following attributes which will draw ticks, labels, etc.

• line
• major_ticks, major_ticklabels
• minor_ticks, minor_ticklabels
• offsetText
• label

### line¶

Derived from Line2d class. Responsible for drawing a spinal(?) line.

### major_ticks, minor_ticks¶

Derived from Line2d class. Note that ticks are markers.

### major_ticklabels, minor_ticklabels¶

Derived from Text. Note that it is not a list of Text artist, but a single artist (similar to a collection).

### axislabel¶

Derived from Text.

### Default AxisArtists¶

By default, following for axis artists are defined.:

ax.axis["left"], ax.axis["bottom"], ax.axis["right"], ax.axis["top"]


The ticklabels and axislabel of the top and the right axis are set to not visible.

For example, if you want to change the color attributes of major_ticklabels of the bottom x-axis

ax.axis["bottom"].major_ticklabels.set_color("b")


Similarly, to make ticklabels invisible

ax.axis["bottom"].major_ticklabels.set_visible(False)


AxisAritst provides a helper method to control the visibility of ticks, ticklabels, and label. To make ticklabel invisible,

ax.axis["bottom"].toggle(ticklabels=False)


To make all of ticks, ticklabels, and (axis) label invisible

ax.axis["bottom"].toggle(all=False)


To turn all off but ticks on

ax.axis["bottom"].toggle(all=False, ticks=True)


To turn all on but (axis) label off

ax.axis["bottom"].toggle(all=True, label=False))


ax.axis’s __getitem__ method can take multiple axis names. For example, to turn ticklabels of “top” and “right” axis on,

ax.axis["top","right"].toggle(ticklabels=True))


Note that ‘ax.axis[“top”,”right”]’ returns a simple proxy object that translate above code to something like below.

for n in ["top","right"]:
ax.axis[n].toggle(ticklabels=True))


So, any return values in the for loop are ignored. And you should not use it anything more than a simple method.

Like the list indexing ”:” means all items, i.e.,

ax.axis[:].major_ticks.set_color("r")


changes tick color in all axis.

## HowTo¶

1. Changing tick locations and label.

Same as the original mpl’s axes.:

ax.set_xticks([1,2,3])

1. Changing axis properties like color, etc.

Change the properties of appropriate artists. For example, to change the color of the ticklabels:

ax.axis["left"].major_ticklabels.set_color("r")

1. To change the attributes of multiple axis:

ax.axis["left","bottom"].major_ticklabels.set_color("r")


or to change the attributes of all axis:

ax.axis[:].major_ticklabels.set_color("r")

2. To change the tick size (length), you need to use

axis.major_ticks.set_ticksize method. To change the direction of the ticks (ticks are in opposite direction of ticklabels by default), use axis.major_ticks.set_tick_out method.

## Rotation and Alignment of TickLabels¶

This is also quite different from the original mpl and can be confusing. When you want to rotate the ticklabels, first consider using “set_axis_direction” method.

ax1.axis["left"].major_ticklabels.set_axis_direction("top")
ax1.axis["right"].label.set_axis_direction("left")


The parameter for set_axis_direction is one of [“left”, “right”, “bottom”, “top”].

You must understand some underlying concept of directions.

1. There is a reference direction which is defined as the direction of the axis line with increasing coordinate. For example, the reference direction of the left x-axis is from bottom to top.

The direction, text angle, and alignments of the ticks, ticklabels and axis-label is determined with respect to the reference direction

1. ticklabel_direction is either the right-hand side (+) of the reference direction or the left-hand side (-).
1. same for the label_direction
1. ticks are by default drawn toward the opposite direction of the ticklabels.
2. text rotation of ticklabels and label is determined in reference to the ticklabel_direction or label_direction, respectively. The rotation of ticklabels and label is anchored.

On the other hand, there is a concept of “axis_direction”. This is a default setting of above properties for each, “bottom”, “left”, “top”, and “right” axis.

 ? ? left bottom right top axislabel direction ‘-‘ ‘+’ ‘+’ ‘-‘ axislabel rotation 180 0 0 180 axislabel va center top center bottom axislabel ha right center right center ticklabel direction ‘-‘ ‘+’ ‘+’ ‘-‘ ticklabels rotation 90 0 -90 180 ticklabel ha right center right center ticklabel va center baseline center baseline

And, ‘set_axis_direction(“top”)’ means to adjust the text rotation etc, for settings suitable for “top” axis. The concept of axis direction can be more clear with curved axis.

The axis_direction can be adjusted in the AxisArtist level, or in the level of its child arists, i.e., ticks, ticklabels, and axis-label.

ax1.axis["left"].set_axis_direction("top")


changes axis_direction of all the associated artist with the “left” axis, while

ax1.axis["left"].major_ticklabels.set_axis_direction("top")


changes the axis_direction of only the major_ticklabels. Note that set_axis_direction in the AxisArtist level changes the ticklabel_direction and label_direction, while changing the axis_direction of ticks, ticklabels, and axis-label does not affect them.

If you want to make ticks outward and ticklabels inside the axes, use invert_ticklabel_direction method.

ax.axis[:].invert_ticklabel_direction()


A related method is “set_tick_out”. It makes ticks outward (as a matter of fact, it makes ticks toward the opposite direction of the default direction).

ax.axis[:].major_ticks.set_tick_out(True)


So, in summary,

• AxisArtist’s methods
• set_axis_direction : “left”, “right”, “bottom”, or “top”
• set_ticklabel_direction : “+” or “-“
• set_axislabel_direction : “+” or “-“
• invert_ticklabel_direction
• Ticks’ methods (major_ticks and minor_ticks)
• set_tick_out : True or False
• set_ticksize : size in points
• TickLabels’ methods (major_ticklabels and minor_ticklabels)
• set_axis_direction : “left”, “right”, “bottom”, or “top”
• set_rotation : angle with respect to the reference direction
• set_ha and set_va : see below
• AxisLabels’ methods (label)
• set_axis_direction : “left”, “right”, “bottom”, or “top”
• set_rotation : angle with respect to the reference direction
• set_ha and set_va

Alignment of TickLabels are treated specially. See below

To change the pad between ticks and ticklabels

ax.axis["left"].major_ticklabels.set_pad(10)


Or ticklabels and axis-label

ax.axis["left"].label.set_pad(10)


## GridHelper¶

To actually define a curvilinear coordinate, you have to use your own grid helper. A generalised version of grid helper class is supplied and this class should suffice in most of cases. A user may provide two functions which defines a transformation (and its inverse pair) from the curved coordinate to (rectilinear) image coordinate. Note that while ticks and grids are drawn for curved coordinate, the data transform of the axes itself (ax.transData) is still rectilinear (image) coordinate.

from  mpl_toolkits.axisartist.grid_helper_curvelinear \
import GridHelperCurveLinear
from mpl_toolkits.axisartist import Subplot

# from curved coordinate to rectlinear coordinate.
def tr(x, y):
x, y = np.asarray(x), np.asarray(y)
return x, y-x

# from rectlinear coordinate to curved coordinate.
def inv_tr(x,y):
x, y = np.asarray(x), np.asarray(y)
return x, y+x

grid_helper = GridHelperCurveLinear((tr, inv_tr))

ax1 = Subplot(fig, 1, 1, 1, grid_helper=grid_helper)



You may use matplotlib’s Transform instance instead (but a inverse transformation must be defined). Often, coordinate range in a curved coordinate system may have a limited range, or may have cycles. In those cases, a more customized version of grid helper is required.

import  mpl_toolkits.axisartist.angle_helper as angle_helper

# PolarAxes.PolarTransform takes radian. However, we want our coordinate
# system in degree
tr = Affine2D().scale(np.pi/180., 1.) + PolarAxes.PolarTransform()

# extreme finder :  find a range of coordinate.
# 20, 20 : number of sampling points along x, y direction
# The first coordinate (longitude, but theta in polar)
#   has a cycle of 360 degree.
# The second coordinate (latitude, but radius in polar)  has a minimum of 0
extreme_finder = angle_helper.ExtremeFinderCycle(20, 20,
lon_cycle = 360,
lat_cycle = None,
lon_minmax = None,
lat_minmax = (0, np.inf),
)

# Find a grid values appropriate for the coordinate (degree,
# minute, second). The argument is a approximate number of grids.
grid_locator1 = angle_helper.LocatorDMS(12)

# And also uses an appropriate formatter.  Note that,the
# acceptable Locator and Formatter class is a bit different than
# that of mpl's, and you cannot directly use mpl's Locator and
# Formatter here (but may be possible in the future).
tick_formatter1 = angle_helper.FormatterDMS()

grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
tick_formatter1=tick_formatter1
)


Again, the transData of the axes is still a rectilinear coordinate (image coordinate). You may manually do conversion between two coordinates, or you may use Parasite Axes for convenience.:

ax1 = SubplotHost(fig, 1, 2, 2, grid_helper=grid_helper)

# A parasite axes with given transform
ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
# note that ax2.transData == tr + ax1.transData
# Anthing you draw in ax2 will match the ticks and grids of ax1.
ax1.parasites.append(ax2)


## FloatingAxis¶

A floating axis is an axis one of whose data coordinate is fixed, i.e, its location is not fixed in Axes coordinate but changes as axes data limits changes. A floating axis can be created using new_floating_axis method. However, it is your responsibility that the resulting AxisArtist is properly added to the axes. A recommended way is to add it as an item of Axes’s axis attribute.:

# floating axis whose first (index starts from 0) coordinate
# (theta) is fixed at 60

ax1.axis["lat"] = axis = ax1.new_floating_axis(0, 60)
axis.label.set_text(r"$\theta = 60^{\circ}$")
axis.label.set_visible(True)