Version 3.1.0
'''
Colorbar toolkit with two classes and a function:
:class:`ColorbarBase`
the base class with full colorbar drawing functionality.
It can be used as-is to make a colorbar for a given colormap;
a mappable object (e.g., image) is not needed.
:class:`Colorbar`
the derived class for use with images or contour plots.
:func:`make_axes`
a function for resizing an axes and adding a second axes
suitable for a colorbar
The :meth:`~matplotlib.figure.Figure.colorbar` method uses :func:`make_axes`
and :class:`Colorbar`; the :func:`~matplotlib.pyplot.colorbar` function
is a thin wrapper over :meth:`~matplotlib.figure.Figure.colorbar`.
'''
import logging
import numpy as np
import matplotlib as mpl
import matplotlib.artist as martist
import matplotlib.cbook as cbook
import matplotlib.collections as collections
import matplotlib.colors as colors
import matplotlib.contour as contour
import matplotlib.cm as cm
import matplotlib.gridspec as gridspec
import matplotlib.patches as mpatches
import matplotlib.path as mpath
import matplotlib.ticker as ticker
import matplotlib.transforms as mtransforms
import matplotlib._layoutbox as layoutbox
import matplotlib._constrained_layout as constrained_layout
from matplotlib import docstring
_log = logging.getLogger(__name__)
make_axes_kw_doc = '''
============= ====================================================
Property Description
============= ====================================================
*orientation* vertical or horizontal
*fraction* 0.15; fraction of original axes to use for colorbar
*pad* 0.05 if vertical, 0.15 if horizontal; fraction
of original axes between colorbar and new image axes
*shrink* 1.0; fraction by which to multiply the size of the colorbar
*aspect* 20; ratio of long to short dimensions
*anchor* (0.0, 0.5) if vertical; (0.5, 1.0) if horizontal;
the anchor point of the colorbar axes
*panchor* (1.0, 0.5) if vertical; (0.5, 0.0) if horizontal;
the anchor point of the colorbar parent axes. If
False, the parent axes' anchor will be unchanged
============= ====================================================
'''
colormap_kw_doc = '''
============ ====================================================
Property Description
============ ====================================================
*extend* [ 'neither' | 'both' | 'min' | 'max' ]
If not 'neither', make pointed end(s) for out-of-
range values. These are set for a given colormap
using the colormap set_under and set_over methods.
*extendfrac* [ *None* | 'auto' | length | lengths ]
If set to *None*, both the minimum and maximum
triangular colorbar extensions with have a length of
5% of the interior colorbar length (this is the
default setting). If set to 'auto', makes the
triangular colorbar extensions the same lengths as
the interior boxes (when *spacing* is set to
'uniform') or the same lengths as the respective
adjacent interior boxes (when *spacing* is set to
'proportional'). If a scalar, indicates the length
of both the minimum and maximum triangular colorbar
extensions as a fraction of the interior colorbar
length. A two-element sequence of fractions may also
be given, indicating the lengths of the minimum and
maximum colorbar extensions respectively as a
fraction of the interior colorbar length.
*extendrect* bool
If *False* the minimum and maximum colorbar extensions
will be triangular (the default). If *True* the
extensions will be rectangular.
*spacing* [ 'uniform' | 'proportional' ]
Uniform spacing gives each discrete color the same
space; proportional makes the space proportional to
the data interval.
*ticks* [ None | list of ticks | Locator object ]
If None, ticks are determined automatically from the
input.
*format* [ None | format string | Formatter object ]
If None, the
:class:`~matplotlib.ticker.ScalarFormatter` is used.
If a format string is given, e.g., '%.3f', that is
used. An alternative
:class:`~matplotlib.ticker.Formatter` object may be
given instead.
*drawedges* bool
Whether to draw lines at color boundaries.
============ ====================================================
The following will probably be useful only in the context of
indexed colors (that is, when the mappable has norm=NoNorm()),
or other unusual circumstances.
============ ===================================================
Property Description
============ ===================================================
*boundaries* None or a sequence
*values* None or a sequence which must be of length 1 less
than the sequence of *boundaries*. For each region
delimited by adjacent entries in *boundaries*, the
color mapped to the corresponding value in values
will be used.
============ ===================================================
'''
colorbar_doc = '''
Add a colorbar to a plot.
Function signatures for the :mod:`~matplotlib.pyplot` interface; all
but the first are also method signatures for the
:meth:`~matplotlib.figure.Figure.colorbar` method::
colorbar(**kwargs)
colorbar(mappable, **kwargs)
colorbar(mappable, cax=cax, **kwargs)
colorbar(mappable, ax=ax, **kwargs)
Parameters
----------
mappable
The `matplotlib.cm.ScalarMappable` (i.e., `~matplotlib.image.Image`,
`~matplotlib.contour.ContourSet`, etc.) described by this colorbar.
This argument is mandatory for the `.Figure.colorbar` method but optional
for the `.pyplot.colorbar` function, which sets the default to the current
image.
Note that one can create a `ScalarMappable` "on-the-fly" to generate
colorbars not attached to a previously drawn artist, e.g. ::
fig.colorbar(cm.ScalarMappable(norm=norm, cmap=cmap), ax=ax)
cax : :class:`~matplotlib.axes.Axes` object, optional
Axes into which the colorbar will be drawn.
ax : :class:`~matplotlib.axes.Axes`, list of Axes, optional
Parent axes from which space for a new colorbar axes will be stolen.
If a list of axes is given they will all be resized to make room for the
colorbar axes.
use_gridspec : bool, optional
If *cax* is ``None``, a new *cax* is created as an instance of Axes. If
*ax* is an instance of Subplot and *use_gridspec* is ``True``, *cax* is
created as an instance of Subplot using the :mod:`~.gridspec` module.
Returns
-------
colorbar : `~matplotlib.colorbar.Colorbar`
See also its base class, `~matplotlib.colorbar.ColorbarBase`. Use
`~.ColorbarBase.set_label` to label the colorbar.
Notes
-----
Additional keyword arguments are of two kinds:
axes properties:
%s
colorbar properties:
%s
If *mappable* is a :class:`~matplotlib.contours.ContourSet`, its *extend*
kwarg is included automatically.
The *shrink* kwarg provides a simple way to scale the colorbar with respect
to the axes. Note that if *cax* is specified, it determines the size of the
colorbar and *shrink* and *aspect* kwargs are ignored.
For more precise control, you can manually specify the positions of
the axes objects in which the mappable and the colorbar are drawn. In
this case, do not use any of the axes properties kwargs.
It is known that some vector graphics viewers (svg and pdf) renders white gaps
between segments of the colorbar. This is due to bugs in the viewers, not
Matplotlib. As a workaround, the colorbar can be rendered with overlapping
segments::
cbar = colorbar()
cbar.solids.set_edgecolor("face")
draw()
However this has negative consequences in other circumstances, e.g. with
semi-transparent images (alpha < 1) and colorbar extensions; therefore, this
workaround is not used by default (see issue #1188).
''' % (make_axes_kw_doc, colormap_kw_doc)
docstring.interpd.update(colorbar_doc=colorbar_doc)
def _set_ticks_on_axis_warn(*args, **kw):
# a top level function which gets put in at the axes'
# set_xticks set_yticks by _patch_ax
cbook._warn_external("Use the colorbar set_ticks() method instead.")
class _ColorbarAutoLocator(ticker.MaxNLocator):
"""
AutoLocator for Colorbar
This locator is just a `.MaxNLocator` except the min and max are
clipped by the norm's min and max (i.e. vmin/vmax from the
image/pcolor/contour object). This is necessary so ticks don't
extrude into the "extend regions".
"""
def __init__(self, colorbar):
"""
This ticker needs to know the *colorbar* so that it can access
its *vmin* and *vmax*. Otherwise it is the same as
`~.ticker.AutoLocator`.
"""
self._colorbar = colorbar
nbins = 'auto'
steps = [1, 2, 2.5, 5, 10]
super().__init__(nbins=nbins, steps=steps)
def tick_values(self, vmin, vmax):
# flip if needed:
if vmin > vmax:
vmin, vmax = vmax, vmin
vmin = max(vmin, self._colorbar.norm.vmin)
vmax = min(vmax, self._colorbar.norm.vmax)
ticks = super().tick_values(vmin, vmax)
rtol = (vmax - vmin) * 1e-10
return ticks[(ticks >= vmin - rtol) & (ticks <= vmax + rtol)]
class _ColorbarAutoMinorLocator(ticker.AutoMinorLocator):
"""
AutoMinorLocator for Colorbar
This locator is just a `.AutoMinorLocator` except the min and max are
clipped by the norm's min and max (i.e. vmin/vmax from the
image/pcolor/contour object). This is necessary so that the minorticks
don't extrude into the "extend regions".
"""
def __init__(self, colorbar, n=None):
"""
This ticker needs to know the *colorbar* so that it can access
its *vmin* and *vmax*.
"""
self._colorbar = colorbar
self.ndivs = n
super().__init__(n=None)
def __call__(self):
vmin = self._colorbar.norm.vmin
vmax = self._colorbar.norm.vmax
ticks = super().__call__()
rtol = (vmax - vmin) * 1e-10
return ticks[(ticks >= vmin - rtol) & (ticks <= vmax + rtol)]
class _ColorbarLogLocator(ticker.LogLocator):
"""
LogLocator for Colorbarbar
This locator is just a `.LogLocator` except the min and max are
clipped by the norm's min and max (i.e. vmin/vmax from the
image/pcolor/contour object). This is necessary so ticks don't
extrude into the "extend regions".
"""
def __init__(self, colorbar, *args, **kwargs):
"""
_ColorbarLogLocator(colorbar, *args, **kwargs)
This ticker needs to know the *colorbar* so that it can access
its *vmin* and *vmax*. Otherwise it is the same as
`~.ticker.LogLocator`. The ``*args`` and ``**kwargs`` are the
same as `~.ticker.LogLocator`.
"""
self._colorbar = colorbar
super().__init__(*args, **kwargs)
def tick_values(self, vmin, vmax):
if vmin > vmax:
vmin, vmax = vmax, vmin
vmin = max(vmin, self._colorbar.norm.vmin)
vmax = min(vmax, self._colorbar.norm.vmax)
ticks = super().tick_values(vmin, vmax)
rtol = (np.log10(vmax) - np.log10(vmin)) * 1e-10
ticks = ticks[(np.log10(ticks) >= np.log10(vmin) - rtol) &
(np.log10(ticks) <= np.log10(vmax) + rtol)]
return ticks
class _ColorbarMappableDummy(object):
"""
Private class to hold deprecated ColorbarBase methods that used to be
inhereted from ScalarMappable.
"""
@cbook.deprecated("3.1", alternative="ScalarMappable.set_norm")
def set_norm(self, norm):
"""
`.colorbar.Colorbar.set_norm` does nothing; set the norm on
the mappable associated with this colorbar.
"""
pass
@cbook.deprecated("3.1", alternative="ScalarMappable.set_cmap")
def set_cmap(self, cmap):
"""
`.colorbar.Colorbar.set_cmap` does nothing; set the norm on
the mappable associated with this colorbar.
"""
pass
@cbook.deprecated("3.1", alternative="ScalarMappable.set_clim")
def set_clim(self, vmin=None, vmax=None):
"""
`.colorbar.Colorbar.set_clim` does nothing; set the limits on
the mappable associated with this colorbar.
"""
pass
@cbook.deprecated("3.1", alternative="ScalarMappable.get_cmap")
def get_cmap(self):
"""
return the colormap
"""
return self.cmap
@cbook.deprecated("3.1", alternative="ScalarMappable.get_clim")
def get_clim(self):
"""
return the min, max of the color limits for image scaling
"""
return self.norm.vmin, self.norm.vmax
[docs]class ColorbarBase(_ColorbarMappableDummy):
'''
Draw a colorbar in an existing axes.
This is a base class for the :class:`Colorbar` class, which is the
basis for the :func:`~matplotlib.pyplot.colorbar` function and the
:meth:`~matplotlib.figure.Figure.colorbar` method, which are the
usual ways of creating a colorbar.
It is also useful by itself for showing a colormap. If the *cmap*
kwarg is given but *boundaries* and *values* are left as None,
then the colormap will be displayed on a 0-1 scale. To show the
under- and over-value colors, specify the *norm* as::
colors.Normalize(clip=False)
To show the colors versus index instead of on the 0-1 scale,
use::
norm=colors.NoNorm.
Useful public methods are :meth:`set_label` and :meth:`add_lines`.
Attributes
----------
ax : Axes
The `Axes` instance in which the colorbar is drawn.
lines : list
A list of `LineCollection` if lines were drawn, otherwise
an empty list.
dividers : LineCollection
A LineCollection if *drawedges* is ``True``, otherwise ``None``.
'''
_slice_dict = {'neither': slice(0, None),
'both': slice(1, -1),
'min': slice(1, None),
'max': slice(0, -1)}
n_rasterize = 50 # rasterize solids if number of colors >= n_rasterize
def __init__(self, ax, cmap=None,
norm=None,
alpha=None,
values=None,
boundaries=None,
orientation='vertical',
ticklocation='auto',
extend='neither',
spacing='uniform', # uniform or proportional
ticks=None,
format=None,
drawedges=False,
filled=True,
extendfrac=None,
extendrect=False,
label='',
):
#: The axes that this colorbar lives in.
self.ax = ax
self._patch_ax()
if cmap is None:
cmap = cm.get_cmap()
if norm is None:
norm = colors.Normalize()
self.alpha = alpha
self.cmap = cmap
self.norm = norm
self.values = values
self.boundaries = boundaries
self.extend = extend
self._inside = self._slice_dict[extend]
self.spacing = spacing
self.orientation = orientation
self.drawedges = drawedges
self.filled = filled
self.extendfrac = extendfrac
self.extendrect = extendrect
self.solids = None
self.lines = list()
self.outline = None
self.patch = None
self.dividers = None
self.locator = None
self.formatter = None
self._manual_tick_data_values = None
if ticklocation == 'auto':
ticklocation = 'bottom' if orientation == 'horizontal' else 'right'
self.ticklocation = ticklocation
self.set_label(label)
self._reset_locator_formatter_scale()
if np.iterable(ticks):
self.locator = ticker.FixedLocator(ticks, nbins=len(ticks))
else:
self.locator = ticks # Handle default in _ticker()
if isinstance(format, str):
self.formatter = ticker.FormatStrFormatter(format)
else:
self.formatter = format # Assume it is a Formatter or None
self.draw_all()
def _extend_lower(self):
"""Return whether the lower limit is open ended."""
return self.extend in ('both', 'min')
def _extend_upper(self):
"""Return whether the uper limit is open ended."""
return self.extend in ('both', 'max')
def _patch_ax(self):
# bind some methods to the axes to warn users
# against using those methods.
self.ax.set_xticks = _set_ticks_on_axis_warn
self.ax.set_yticks = _set_ticks_on_axis_warn
[docs] def draw_all(self):
'''
Calculate any free parameters based on the current cmap and norm,
and do all the drawing.
'''
# sets self._boundaries and self._values in real data units.
# takes into account extend values:
self._process_values()
# sets self.vmin and vmax in data units, but just for
# the part of the colorbar that is not part of the extend
# patch:
self._find_range()
# returns the X and Y mesh, *but* this was/is in normalized
# units:
X, Y = self._mesh()
C = self._values[:, np.newaxis]
self.config_axis()
self._config_axes(X, Y)
if self.filled:
self._add_solids(X, Y, C)
[docs] def config_axis(self):
ax = self.ax
if self.orientation == 'vertical':
long_axis, short_axis = ax.yaxis, ax.xaxis
else:
long_axis, short_axis = ax.xaxis, ax.yaxis
long_axis.set_label_position(self.ticklocation)
long_axis.set_ticks_position(self.ticklocation)
short_axis.set_ticks([])
short_axis.set_ticks([], minor=True)
self._set_label()
def _get_ticker_locator_formatter(self):
"""
This code looks at the norm being used by the colorbar
and decides what locator and formatter to use. If ``locator`` has
already been set by hand, it just returns
``self.locator, self.formatter``.
"""
locator = self.locator
formatter = self.formatter
if locator is None:
if self.boundaries is None:
if isinstance(self.norm, colors.NoNorm):
nv = len(self._values)
base = 1 + int(nv / 10)
locator = ticker.IndexLocator(base=base, offset=0)
elif isinstance(self.norm, colors.BoundaryNorm):
b = self.norm.boundaries
locator = ticker.FixedLocator(b, nbins=10)
elif isinstance(self.norm, colors.LogNorm):
locator = _ColorbarLogLocator(self)
elif isinstance(self.norm, colors.SymLogNorm):
# The subs setting here should be replaced
# by logic in the locator.
locator = ticker.SymmetricalLogLocator(
subs=np.arange(1, 10),
linthresh=self.norm.linthresh,
base=10)
else:
if mpl.rcParams['_internal.classic_mode']:
locator = ticker.MaxNLocator()
else:
locator = _ColorbarAutoLocator(self)
else:
b = self._boundaries[self._inside]
locator = ticker.FixedLocator(b, nbins=10)
if formatter is None:
if isinstance(self.norm, colors.LogNorm):
formatter = ticker.LogFormatterSciNotation()
elif isinstance(self.norm, colors.SymLogNorm):
formatter = ticker.LogFormatterSciNotation(
linthresh=self.norm.linthresh)
else:
formatter = ticker.ScalarFormatter()
else:
formatter = self.formatter
self.locator = locator
self.formatter = formatter
_log.debug('locator: %r', locator)
return locator, formatter
def _use_auto_colorbar_locator(self):
"""
Return if we should use an adjustable tick locator or a fixed
one. (check is used twice so factored out here...)
"""
return (self.boundaries is None
and self.values is None
and ((type(self.norm) == colors.Normalize)
or (type(self.norm) == colors.LogNorm)))
def _reset_locator_formatter_scale(self):
"""
Reset the locator et al to defaults. Any user-hardcoded changes
need to be re-entered if this gets called (either at init, or when
the mappable normal gets changed: Colorbar.update_normal)
"""
self.locator = None
self.formatter = None
if (isinstance(self.norm, colors.LogNorm)
and self._use_auto_colorbar_locator()):
# *both* axes are made log so that determining the
# mid point is easier.
self.ax.set_xscale('log')
self.ax.set_yscale('log')
self.minorticks_on()
else:
self.ax.set_xscale('linear')
self.ax.set_yscale('linear')
[docs] def update_ticks(self):
"""
Force the update of the ticks and ticklabels. This must be
called whenever the tick locator and/or tick formatter changes.
"""
ax = self.ax
# get the locator and formatter. Defaults to
# self.locator if not None..
locator, formatter = self._get_ticker_locator_formatter()
if self.orientation == 'vertical':
long_axis, short_axis = ax.yaxis, ax.xaxis
else:
long_axis, short_axis = ax.xaxis, ax.yaxis
if self._use_auto_colorbar_locator():
_log.debug('Using auto colorbar locator on colorbar')
_log.debug('locator: %r', locator)
long_axis.set_major_locator(locator)
long_axis.set_major_formatter(formatter)
else:
_log.debug('Using fixed locator on colorbar')
ticks, ticklabels, offset_string = self._ticker(locator, formatter)
long_axis.set_ticks(ticks)
long_axis.set_ticklabels(ticklabels)
long_axis.get_major_formatter().set_offset_string(offset_string)
[docs] def set_ticks(self, ticks, update_ticks=True):
"""
Set tick locations.
Parameters
----------
ticks : {None, sequence, :class:`~matplotlib.ticker.Locator` instance}
If None, a default Locator will be used.
update_ticks : {True, False}, optional
If True, tick locations are updated immediately. If False,
use :meth:`update_ticks` to manually update the ticks.
"""
if np.iterable(ticks):
self.locator = ticker.FixedLocator(ticks, nbins=len(ticks))
else:
self.locator = ticks
if update_ticks:
self.update_ticks()
self.stale = True
[docs] def get_ticks(self, minor=False):
"""Return the x ticks as a list of locations."""
if self._manual_tick_data_values is None:
ax = self.ax
if self.orientation == 'vertical':
long_axis, short_axis = ax.yaxis, ax.xaxis
else:
long_axis, short_axis = ax.xaxis, ax.yaxis
return long_axis.get_majorticklocs()
else:
# We made the axes manually, the old way, and the ylim is 0-1,
# so the majorticklocs are in those units, not data units.
return self._manual_tick_data_values
[docs] def set_ticklabels(self, ticklabels, update_ticks=True):
"""
Set tick labels.
Tick labels are updated immediately unless *update_ticks* is *False*,
in which case one should call `.update_ticks` explicitly.
"""
if isinstance(self.locator, ticker.FixedLocator):
self.formatter = ticker.FixedFormatter(ticklabels)
if update_ticks:
self.update_ticks()
else:
cbook._warn_external("set_ticks() must have been called.")
self.stale = True
[docs] def minorticks_on(self):
"""
Turns on the minor ticks on the colorbar without extruding
into the "extend regions".
"""
ax = self.ax
long_axis = ax.yaxis if self.orientation == 'vertical' else ax.xaxis
if long_axis.get_scale() == 'log':
long_axis.set_minor_locator(_ColorbarLogLocator(self, base=10.,
subs='auto'))
long_axis.set_minor_formatter(ticker.LogFormatterSciNotation())
else:
long_axis.set_minor_locator(_ColorbarAutoMinorLocator(self))
[docs] def minorticks_off(self):
"""
Turns off the minor ticks on the colorbar.
"""
ax = self.ax
long_axis = ax.yaxis if self.orientation == 'vertical' else ax.xaxis
long_axis.set_minor_locator(ticker.NullLocator())
def _config_axes(self, X, Y):
'''
Make an axes patch and outline.
'''
ax = self.ax
ax.set_frame_on(False)
ax.set_navigate(False)
xy = self._outline(X, Y)
ax.ignore_existing_data_limits = True
ax.update_datalim(xy)
ax.set_xlim(*ax.dataLim.intervalx)
ax.set_ylim(*ax.dataLim.intervaly)
if self.outline is not None:
self.outline.remove()
self.outline = mpatches.Polygon(
xy, edgecolor=mpl.rcParams['axes.edgecolor'],
facecolor='none',
linewidth=mpl.rcParams['axes.linewidth'],
closed=True,
zorder=2)
ax.add_artist(self.outline)
self.outline.set_clip_box(None)
self.outline.set_clip_path(None)
c = mpl.rcParams['axes.facecolor']
if self.patch is not None:
self.patch.remove()
self.patch = mpatches.Polygon(xy, edgecolor=c,
facecolor=c,
linewidth=0.01,
zorder=-1)
ax.add_artist(self.patch)
self.update_ticks()
def _set_label(self):
if self.orientation == 'vertical':
self.ax.set_ylabel(self._label, **self._labelkw)
else:
self.ax.set_xlabel(self._label, **self._labelkw)
self.stale = True
[docs] def set_label(self, label, **kw):
"""Label the long axis of the colorbar."""
self._label = str(label)
self._labelkw = kw
self._set_label()
def _outline(self, X, Y):
'''
Return *x*, *y* arrays of colorbar bounding polygon,
taking orientation into account.
'''
N = X.shape[0]
ii = [0, 1, N - 2, N - 1, 2 * N - 1, 2 * N - 2, N + 1, N, 0]
x = X.T.reshape(-1)[ii]
y = Y.T.reshape(-1)[ii]
return (np.column_stack([y, x])
if self.orientation == 'horizontal' else
np.column_stack([x, y]))
def _edges(self, X, Y):
'''
Return the separator line segments; helper for _add_solids.
'''
N = X.shape[0]
# Using the non-array form of these line segments is much
# simpler than making them into arrays.
if self.orientation == 'vertical':
return [list(zip(X[i], Y[i])) for i in range(1, N - 1)]
else:
return [list(zip(Y[i], X[i])) for i in range(1, N - 1)]
def _add_solids(self, X, Y, C):
'''
Draw the colors using :meth:`~matplotlib.axes.Axes.pcolormesh`;
optionally add separators.
'''
if self.orientation == 'vertical':
args = (X, Y, C)
else:
args = (np.transpose(Y), np.transpose(X), np.transpose(C))
kw = dict(cmap=self.cmap,
norm=self.norm,
alpha=self.alpha,
edgecolors='None')
_log.debug('Setting pcolormesh')
col = self.ax.pcolormesh(*args, **kw)
# self.add_observer(col) # We should observe, not be observed...
if self.solids is not None:
self.solids.remove()
self.solids = col
if self.dividers is not None:
self.dividers.remove()
self.dividers = None
if self.drawedges:
linewidths = (0.5 * mpl.rcParams['axes.linewidth'],)
self.dividers = collections.LineCollection(
self._edges(X, Y),
colors=(mpl.rcParams['axes.edgecolor'],),
linewidths=linewidths)
self.ax.add_collection(self.dividers)
elif len(self._y) >= self.n_rasterize:
self.solids.set_rasterized(True)
[docs] def add_lines(self, levels, colors, linewidths, erase=True):
'''
Draw lines on the colorbar.
*colors* and *linewidths* must be scalars or
sequences the same length as *levels*.
Set *erase* to False to add lines without first
removing any previously added lines.
'''
y = self._locate(levels)
rtol = (self._y[-1] - self._y[0]) * 1e-10
igood = (y < self._y[-1] + rtol) & (y > self._y[0] - rtol)
y = y[igood]
if np.iterable(colors):
colors = np.asarray(colors)[igood]
if np.iterable(linewidths):
linewidths = np.asarray(linewidths)[igood]
X, Y = np.meshgrid([self._y[0], self._y[-1]], y)
if self.orientation == 'vertical':
xy = np.stack([X, Y], axis=-1)
else:
xy = np.stack([Y, X], axis=-1)
col = collections.LineCollection(xy, linewidths=linewidths)
if erase and self.lines:
for lc in self.lines:
lc.remove()
self.lines = []
self.lines.append(col)
col.set_color(colors)
self.ax.add_collection(col)
self.stale = True
def _ticker(self, locator, formatter):
'''
Return the sequence of ticks (colorbar data locations),
ticklabels (strings), and the corresponding offset string.
'''
if isinstance(self.norm, colors.NoNorm) and self.boundaries is None:
intv = self._values[0], self._values[-1]
else:
intv = self.vmin, self.vmax
locator.create_dummy_axis(minpos=intv[0])
formatter.create_dummy_axis(minpos=intv[0])
locator.set_view_interval(*intv)
locator.set_data_interval(*intv)
formatter.set_view_interval(*intv)
formatter.set_data_interval(*intv)
b = np.array(locator())
if isinstance(locator, ticker.LogLocator):
eps = 1e-10
b = b[(b <= intv[1] * (1 + eps)) & (b >= intv[0] * (1 - eps))]
else:
eps = (intv[1] - intv[0]) * 1e-10
b = b[(b <= intv[1] + eps) & (b >= intv[0] - eps)]
self._manual_tick_data_values = b
ticks = self._locate(b)
ticklabels = formatter.format_ticks(b)
offset_string = formatter.get_offset()
return ticks, ticklabels, offset_string
def _process_values(self, b=None):
'''
Set the :attr:`_boundaries` and :attr:`_values` attributes
based on the input boundaries and values. Input boundaries
can be *self.boundaries* or the argument *b*.
'''
if b is None:
b = self.boundaries
if b is not None:
self._boundaries = np.asarray(b, dtype=float)
if self.values is None:
self._values = 0.5 * (self._boundaries[:-1]
+ self._boundaries[1:])
if isinstance(self.norm, colors.NoNorm):
self._values = (self._values + 0.00001).astype(np.int16)
else:
self._values = np.array(self.values)
return
if self.values is not None:
self._values = np.array(self.values)
if self.boundaries is None:
b = np.zeros(len(self.values) + 1)
b[1:-1] = 0.5 * (self._values[:-1] + self._values[1:])
b[0] = 2.0 * b[1] - b[2]
b[-1] = 2.0 * b[-2] - b[-3]
self._boundaries = b
return
self._boundaries = np.array(self.boundaries)
return
# Neither boundaries nor values are specified;
# make reasonable ones based on cmap and norm.
if isinstance(self.norm, colors.NoNorm):
b = self._uniform_y(self.cmap.N + 1) * self.cmap.N - 0.5
v = np.zeros(len(b) - 1, dtype=np.int16)
v[self._inside] = np.arange(self.cmap.N, dtype=np.int16)
if self._extend_lower():
v[0] = -1
if self._extend_upper():
v[-1] = self.cmap.N
self._boundaries = b
self._values = v
return
elif isinstance(self.norm, colors.BoundaryNorm):
b = list(self.norm.boundaries)
if self._extend_lower():
b = [b[0] - 1] + b
if self._extend_upper():
b = b + [b[-1] + 1]
b = np.array(b)
v = np.zeros(len(b) - 1)
bi = self.norm.boundaries
v[self._inside] = 0.5 * (bi[:-1] + bi[1:])
if self._extend_lower():
v[0] = b[0] - 1
if self._extend_upper():
v[-1] = b[-1] + 1
self._boundaries = b
self._values = v
return
else:
if not self.norm.scaled():
self.norm.vmin = 0
self.norm.vmax = 1
self.norm.vmin, self.norm.vmax = mtransforms.nonsingular(
self.norm.vmin,
self.norm.vmax,
expander=0.1)
b = self.norm.inverse(self._uniform_y(self.cmap.N + 1))
if isinstance(self.norm, (colors.PowerNorm, colors.LogNorm)):
# If using a lognorm or powernorm, ensure extensions don't
# go negative
if self._extend_lower():
b[0] = 0.9 * b[0]
if self._extend_upper():
b[-1] = 1.1 * b[-1]
else:
if self._extend_lower():
b[0] = b[0] - 1
if self._extend_upper():
b[-1] = b[-1] + 1
self._process_values(b)
def _find_range(self):
'''
Set :attr:`vmin` and :attr:`vmax` attributes to the first and
last boundary excluding extended end boundaries.
'''
b = self._boundaries[self._inside]
self.vmin = b[0]
self.vmax = b[-1]
def _central_N(self):
"""Return the number of boundaries excluding end extensions."""
nb = len(self._boundaries)
if self.extend == 'both':
nb -= 2
elif self.extend in ('min', 'max'):
nb -= 1
return nb
def _extended_N(self):
'''
Based on the colormap and extend variable, return the
number of boundaries.
'''
N = self.cmap.N + 1
if self.extend == 'both':
N += 2
elif self.extend in ('min', 'max'):
N += 1
return N
def _get_extension_lengths(self, frac, automin, automax, default=0.05):
'''
Get the lengths of colorbar extensions.
A helper method for _uniform_y and _proportional_y.
'''
# Set the default value.
extendlength = np.array([default, default])
if isinstance(frac, str):
if frac.lower() == 'auto':
# Use the provided values when 'auto' is required.
extendlength[:] = [automin, automax]
else:
# Any other string is invalid.
raise ValueError('invalid value for extendfrac')
elif frac is not None:
try:
# Try to set min and max extension fractions directly.
extendlength[:] = frac
# If frac is a sequence containing None then NaN may
# be encountered. This is an error.
if np.isnan(extendlength).any():
raise ValueError()
except (TypeError, ValueError):
# Raise an error on encountering an invalid value for frac.
raise ValueError('invalid value for extendfrac')
return extendlength
def _uniform_y(self, N):
'''
Return colorbar data coordinates for *N* uniformly
spaced boundaries, plus ends if required.
'''
if self.extend == 'neither':
y = np.linspace(0, 1, N)
else:
automin = automax = 1. / (N - 1.)
extendlength = self._get_extension_lengths(self.extendfrac,
automin, automax,
default=0.05)
if self.extend == 'both':
y = np.zeros(N + 2, 'd')
y[0] = 0. - extendlength[0]
y[-1] = 1. + extendlength[1]
elif self.extend == 'min':
y = np.zeros(N + 1, 'd')
y[0] = 0. - extendlength[0]
else:
y = np.zeros(N + 1, 'd')
y[-1] = 1. + extendlength[1]
y[self._inside] = np.linspace(0, 1, N)
return y
def _proportional_y(self):
'''
Return colorbar data coordinates for the boundaries of
a proportional colorbar.
'''
if isinstance(self.norm, colors.BoundaryNorm):
y = (self._boundaries - self._boundaries[0])
y = y / (self._boundaries[-1] - self._boundaries[0])
else:
y = self.norm(self._boundaries.copy())
y = np.ma.filled(y, np.nan)
if self.extend == 'min':
# Exclude leftmost interval of y.
clen = y[-1] - y[1]
automin = (y[2] - y[1]) / clen
automax = (y[-1] - y[-2]) / clen
elif self.extend == 'max':
# Exclude rightmost interval in y.
clen = y[-2] - y[0]
automin = (y[1] - y[0]) / clen
automax = (y[-2] - y[-3]) / clen
elif self.extend == 'both':
# Exclude leftmost and rightmost intervals in y.
clen = y[-2] - y[1]
automin = (y[2] - y[1]) / clen
automax = (y[-2] - y[-3]) / clen
if self.extend in ('both', 'min', 'max'):
extendlength = self._get_extension_lengths(self.extendfrac,
automin, automax,
default=0.05)
if self.extend in ('both', 'min'):
y[0] = 0. - extendlength[0]
if self.extend in ('both', 'max'):
y[-1] = 1. + extendlength[1]
yi = y[self._inside]
norm = colors.Normalize(yi[0], yi[-1])
y[self._inside] = np.ma.filled(norm(yi), np.nan)
return y
def _mesh(self):
'''
Return X,Y, the coordinate arrays for the colorbar pcolormesh.
These are suitable for a vertical colorbar; swapping and
transposition for a horizontal colorbar are done outside
this function.
'''
# if boundaries and values are None, then we can go ahead and
# scale this up for Auto tick location. Otherwise we
# want to keep normalized between 0 and 1 and use manual tick
# locations.
x = np.array([0.0, 1.0])
if self.spacing == 'uniform':
y = self._uniform_y(self._central_N())
else:
y = self._proportional_y()
if self._use_auto_colorbar_locator():
y = self.norm.inverse(y)
x = self.norm.inverse(x)
self._y = y
X, Y = np.meshgrid(x, y)
if self._use_auto_colorbar_locator():
xmid = self.norm.inverse(0.5)
else:
xmid = 0.5
if self._extend_lower() and not self.extendrect:
X[0, :] = xmid
if self._extend_upper() and not self.extendrect:
X[-1, :] = xmid
return X, Y
def _locate(self, x):
'''
Given a set of color data values, return their
corresponding colorbar data coordinates.
'''
if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)):
b = self._boundaries
xn = x
else:
# Do calculations using normalized coordinates so
# as to make the interpolation more accurate.
b = self.norm(self._boundaries, clip=False).filled()
xn = self.norm(x, clip=False).filled()
bunique = b
yunique = self._y
# trim extra b values at beginning and end if they are
# not unique. These are here for extended colorbars, and are not
# wanted for the interpolation.
if b[0] == b[1]:
bunique = bunique[1:]
yunique = yunique[1:]
if b[-1] == b[-2]:
bunique = bunique[:-1]
yunique = yunique[:-1]
z = np.interp(xn, bunique, yunique)
return z
[docs] def remove(self):
"""
Remove this colorbar from the figure
"""
fig = self.ax.figure
fig.delaxes(self.ax)
[docs]class Colorbar(ColorbarBase):
"""
This class connects a :class:`ColorbarBase` to a
:class:`~matplotlib.cm.ScalarMappable` such as a
:class:`~matplotlib.image.AxesImage` generated via
:meth:`~matplotlib.axes.Axes.imshow`.
It is not intended to be instantiated directly; instead,
use :meth:`~matplotlib.figure.Figure.colorbar` or
:func:`~matplotlib.pyplot.colorbar` to make your colorbar.
"""
def __init__(self, ax, mappable, **kw):
# Ensure the given mappable's norm has appropriate vmin and vmax set
# even if mappable.draw has not yet been called.
if mappable.get_array() is not None:
mappable.autoscale_None()
self.mappable = mappable
kw['cmap'] = cmap = mappable.cmap
kw['norm'] = mappable.norm
if isinstance(mappable, contour.ContourSet):
CS = mappable
kw['alpha'] = mappable.get_alpha()
kw['boundaries'] = CS._levels
kw['values'] = CS.cvalues
kw['extend'] = CS.extend
kw.setdefault('ticks', ticker.FixedLocator(CS.levels, nbins=10))
kw['filled'] = CS.filled
ColorbarBase.__init__(self, ax, **kw)
if not CS.filled:
self.add_lines(CS)
else:
if getattr(cmap, 'colorbar_extend', False) is not False:
kw.setdefault('extend', cmap.colorbar_extend)
if isinstance(mappable, martist.Artist):
kw['alpha'] = mappable.get_alpha()
ColorbarBase.__init__(self, ax, **kw)
[docs] def on_mappable_changed(self, mappable):
"""
Updates this colorbar to match the mappable's properties.
Typically this is automatically registered as an event handler
by :func:`colorbar_factory` and should not be called manually.
"""
_log.debug('colorbar mappable changed')
self.update_normal(mappable)
[docs] def add_lines(self, CS, erase=True):
'''
Add the lines from a non-filled
:class:`~matplotlib.contour.ContourSet` to the colorbar.
Set *erase* to False if these lines should be added to
any pre-existing lines.
'''
if not isinstance(CS, contour.ContourSet) or CS.filled:
raise ValueError('add_lines is only for a ContourSet of lines')
tcolors = [c[0] for c in CS.tcolors]
tlinewidths = [t[0] for t in CS.tlinewidths]
# The following was an attempt to get the colorbar lines
# to follow subsequent changes in the contour lines,
# but more work is needed: specifically, a careful
# look at event sequences, and at how
# to make one object track another automatically.
#tcolors = [col.get_colors()[0] for col in CS.collections]
#tlinewidths = [col.get_linewidth()[0] for lw in CS.collections]
ColorbarBase.add_lines(self, CS.levels, tcolors, tlinewidths,
erase=erase)
[docs] def update_normal(self, mappable):
"""
Update solid patches, lines, etc.
Unlike `.update_bruteforce`, this does not clear the axes. This is
meant to be called when the norm of the image or contour plot to which
this colorbar belongs changes.
If the norm on the mappable is different than before, this resets the
locator and formatter for the axis, so if these have been customized,
they will need to be customized again. However, if the norm only
changes values of *vmin*, *vmax* or *cmap* then the old formatter
and locator will be preserved.
"""
_log.debug('colorbar update normal %r %r', mappable.norm, self.norm)
self.mappable = mappable
self.set_alpha(mappable.get_alpha())
self.cmap = mappable.cmap
if mappable.norm != self.norm:
self.norm = mappable.norm
self._reset_locator_formatter_scale()
self.draw_all()
if isinstance(self.mappable, contour.ContourSet):
CS = self.mappable
if not CS.filled:
self.add_lines(CS)
self.stale = True
[docs] def update_bruteforce(self, mappable):
'''
Destroy and rebuild the colorbar. This is
intended to become obsolete, and will probably be
deprecated and then removed. It is not called when
the pyplot.colorbar function or the Figure.colorbar
method are used to create the colorbar.
'''
# We are using an ugly brute-force method: clearing and
# redrawing the whole thing. The problem is that if any
# properties have been changed by methods other than the
# colorbar methods, those changes will be lost.
self.ax.cla()
self.locator = None
self.formatter = None
# clearing the axes will delete outline, patch, solids, and lines:
self.outline = None
self.patch = None
self.solids = None
self.lines = list()
self.dividers = None
self.update_normal(mappable)
self.draw_all()
if isinstance(self.mappable, contour.ContourSet):
CS = self.mappable
if not CS.filled:
self.add_lines(CS)
#if self.lines is not None:
# tcolors = [c[0] for c in CS.tcolors]
# self.lines.set_color(tcolors)
#Fixme? Recalculate boundaries, ticks if vmin, vmax have changed.
#Fixme: Some refactoring may be needed; we should not
# be recalculating everything if there was a simple alpha
# change.
[docs] def remove(self):
"""
Remove this colorbar from the figure. If the colorbar was created with
``use_gridspec=True`` then restore the gridspec to its previous value.
"""
ColorbarBase.remove(self)
self.mappable.callbacksSM.disconnect(self.mappable.colorbar_cid)
self.mappable.colorbar = None
self.mappable.colorbar_cid = None
try:
ax = self.mappable.axes
except AttributeError:
return
try:
gs = ax.get_subplotspec().get_gridspec()
subplotspec = gs.get_topmost_subplotspec()
except AttributeError:
# use_gridspec was False
pos = ax.get_position(original=True)
ax._set_position(pos)
else:
# use_gridspec was True
ax.set_subplotspec(subplotspec)
[docs]@docstring.Substitution(make_axes_kw_doc)
def make_axes(parents, location=None, orientation=None, fraction=0.15,
shrink=1.0, aspect=20, **kw):
'''
Resize and reposition parent axes, and return a child
axes suitable for a colorbar.
Keyword arguments may include the following (with defaults):
location : [None|'left'|'right'|'top'|'bottom']
The position, relative to **parents**, where the colorbar axes
should be created. If None, the value will either come from the
given ``orientation``, else it will default to 'right'.
orientation : [None|'vertical'|'horizontal']
The orientation of the colorbar. Typically, this keyword shouldn't
be used, as it can be derived from the ``location`` keyword.
%s
Returns (cax, kw), the child axes and the reduced kw dictionary to be
passed when creating the colorbar instance.
'''
locations = ["left", "right", "top", "bottom"]
if orientation is not None and location is not None:
raise TypeError('position and orientation are mutually exclusive. '
'Consider setting the position to any of {}'
.format(', '.join(locations)))
# provide a default location
if location is None and orientation is None:
location = 'right'
# allow the user to not specify the location by specifying the
# orientation instead
if location is None:
location = 'right' if orientation == 'vertical' else 'bottom'
if location not in locations:
raise ValueError('Invalid colorbar location. Must be one '
'of %s' % ', '.join(locations))
default_location_settings = {'left': {'anchor': (1.0, 0.5),
'panchor': (0.0, 0.5),
'pad': 0.10,
'orientation': 'vertical'},
'right': {'anchor': (0.0, 0.5),
'panchor': (1.0, 0.5),
'pad': 0.05,
'orientation': 'vertical'},
'top': {'anchor': (0.5, 0.0),
'panchor': (0.5, 1.0),
'pad': 0.05,
'orientation': 'horizontal'},
'bottom': {'anchor': (0.5, 1.0),
'panchor': (0.5, 0.0),
'pad': 0.15, # backwards compat
'orientation': 'horizontal'},
}
loc_settings = default_location_settings[location]
# put appropriate values into the kw dict for passing back to
# the Colorbar class
kw['orientation'] = loc_settings['orientation']
kw['ticklocation'] = location
anchor = kw.pop('anchor', loc_settings['anchor'])
parent_anchor = kw.pop('panchor', loc_settings['panchor'])
parents_iterable = np.iterable(parents)
# turn parents into a list if it is not already. We do this w/ np
# because `plt.subplots` can return an ndarray and is natural to
# pass to `colorbar`.
parents = np.atleast_1d(parents).ravel()
# check if using constrained_layout:
try:
gs = parents[0].get_subplotspec().get_gridspec()
using_constrained_layout = (gs._layoutbox is not None)
except AttributeError:
using_constrained_layout = False
# defaults are not appropriate for constrained_layout:
pad0 = loc_settings['pad']
if using_constrained_layout:
pad0 = 0.02
pad = kw.pop('pad', pad0)
fig = parents[0].get_figure()
if not all(fig is ax.get_figure() for ax in parents):
raise ValueError('Unable to create a colorbar axes as not all '
'parents share the same figure.')
# take a bounding box around all of the given axes
parents_bbox = mtransforms.Bbox.union(
[ax.get_position(original=True).frozen() for ax in parents])
pb = parents_bbox
if location in ('left', 'right'):
if location == 'left':
pbcb, _, pb1 = pb.splitx(fraction, fraction + pad)
else:
pb1, _, pbcb = pb.splitx(1 - fraction - pad, 1 - fraction)
pbcb = pbcb.shrunk(1.0, shrink).anchored(anchor, pbcb)
else:
if location == 'bottom':
pbcb, _, pb1 = pb.splity(fraction, fraction + pad)
else:
pb1, _, pbcb = pb.splity(1 - fraction - pad, 1 - fraction)
pbcb = pbcb.shrunk(shrink, 1.0).anchored(anchor, pbcb)
# define the aspect ratio in terms of y's per x rather than x's per y
aspect = 1.0 / aspect
# define a transform which takes us from old axes coordinates to
# new axes coordinates
shrinking_trans = mtransforms.BboxTransform(parents_bbox, pb1)
# transform each of the axes in parents using the new transform
for ax in parents:
new_posn = shrinking_trans.transform(ax.get_position(original=True))
new_posn = mtransforms.Bbox(new_posn)
ax._set_position(new_posn)
if parent_anchor is not False:
ax.set_anchor(parent_anchor)
cax = fig.add_axes(pbcb, label="<colorbar>")
# OK, now make a layoutbox for the cb axis. Later, we will use this
# to make the colorbar fit nicely.
if not using_constrained_layout:
# no layout boxes:
lb = None
lbpos = None
# and we need to set the aspect ratio by hand...
cax.set_aspect(aspect, anchor=anchor, adjustable='box')
else:
if not parents_iterable:
# this is a single axis...
ax = parents[0]
lb, lbpos = constrained_layout.layoutcolorbarsingle(
ax, cax, shrink, aspect, location, pad=pad)
else: # there is more than one parent, so lets use gridspec
# the colorbar will be a sibling of this gridspec, so the
# parent is the same parent as the gridspec. Either the figure,
# or a subplotspec.
lb, lbpos = constrained_layout.layoutcolorbargridspec(
parents, cax, shrink, aspect, location, pad)
cax._layoutbox = lb
cax._poslayoutbox = lbpos
return cax, kw
[docs]@docstring.Substitution(make_axes_kw_doc)
def make_axes_gridspec(parent, *, fraction=0.15, shrink=1.0, aspect=20, **kw):
'''
Resize and reposition a parent axes, and return a child axes
suitable for a colorbar. This function is similar to
make_axes. Prmary differences are
* *make_axes_gridspec* only handles the *orientation* keyword
and cannot handle the "location" keyword.
* *make_axes_gridspec* should only be used with a subplot parent.
* *make_axes* creates an instance of Axes. *make_axes_gridspec*
creates an instance of Subplot.
* *make_axes* updates the position of the
parent. *make_axes_gridspec* replaces the grid_spec attribute
of the parent with a new one.
While this function is meant to be compatible with *make_axes*,
there could be some minor differences.
Keyword arguments may include the following (with defaults):
*orientation*
'vertical' or 'horizontal'
%s
All but the first of these are stripped from the input kw set.
Returns (cax, kw), the child axes and the reduced kw dictionary to be
passed when creating the colorbar instance.
'''
orientation = kw.setdefault('orientation', 'vertical')
kw['ticklocation'] = 'auto'
x1 = 1 - fraction
# for shrinking
pad_s = (1 - shrink) * 0.5
wh_ratios = [pad_s, shrink, pad_s]
# we need to none the tree of layoutboxes because
# constrained_layout can't remove and replace the tree
# hierarchy w/o a seg fault.
gs = parent.get_subplotspec().get_gridspec()
layoutbox.nonetree(gs._layoutbox)
gs_from_subplotspec = gridspec.GridSpecFromSubplotSpec
if orientation == 'vertical':
pad = kw.pop('pad', 0.05)
wh_space = 2 * pad / (1 - pad)
gs = gs_from_subplotspec(1, 2,
subplot_spec=parent.get_subplotspec(),
wspace=wh_space,
width_ratios=[x1 - pad, fraction])
gs2 = gs_from_subplotspec(3, 1,
subplot_spec=gs[1],
hspace=0.,
height_ratios=wh_ratios)
anchor = (0.0, 0.5)
panchor = (1.0, 0.5)
else:
pad = kw.pop('pad', 0.15)
wh_space = 2 * pad / (1 - pad)
gs = gs_from_subplotspec(2, 1,
subplot_spec=parent.get_subplotspec(),
hspace=wh_space,
height_ratios=[x1 - pad, fraction])
gs2 = gs_from_subplotspec(1, 3,
subplot_spec=gs[1],
wspace=0.,
width_ratios=wh_ratios)
aspect = 1 / aspect
anchor = (0.5, 1.0)
panchor = (0.5, 0.0)
parent.set_subplotspec(gs[0])
parent.update_params()
parent._set_position(parent.figbox)
parent.set_anchor(panchor)
fig = parent.get_figure()
cax = fig.add_subplot(gs2[1], label="<colorbar>")
cax.set_aspect(aspect, anchor=anchor, adjustable='box')
return cax, kw
[docs]class ColorbarPatch(Colorbar):
"""
A Colorbar which is created using :class:`~matplotlib.patches.Patch`
rather than the default :func:`~matplotlib.axes.pcolor`.
It uses a list of Patch instances instead of a
:class:`~matplotlib.collections.PatchCollection` because the
latter does not allow the hatch pattern to vary among the
members of the collection.
"""
def __init__(self, ax, mappable, **kw):
# we do not want to override the behaviour of solids
# so add a new attribute which will be a list of the
# colored patches in the colorbar
self.solids_patches = []
Colorbar.__init__(self, ax, mappable, **kw)
def _add_solids(self, X, Y, C):
"""
Draw the colors using :class:`~matplotlib.patches.Patch`;
optionally add separators.
"""
n_segments = len(C)
# ensure there are sufficient hatches
hatches = self.mappable.hatches * n_segments
patches = []
for i in range(len(X) - 1):
val = C[i][0]
hatch = hatches[i]
xy = np.array([[X[i][0], Y[i][0]],
[X[i][1], Y[i][0]],
[X[i + 1][1], Y[i + 1][0]],
[X[i + 1][0], Y[i + 1][1]]])
if self.orientation == 'horizontal':
# if horizontal swap the xs and ys
xy = xy[..., ::-1]
patch = mpatches.PathPatch(mpath.Path(xy),
facecolor=self.cmap(self.norm(val)),
hatch=hatch, linewidth=0,
antialiased=False, alpha=self.alpha)
self.ax.add_patch(patch)
patches.append(patch)
if self.solids_patches:
for solid in self.solids_patches:
solid.remove()
self.solids_patches = patches
if self.dividers is not None:
self.dividers.remove()
self.dividers = None
if self.drawedges:
self.dividers = collections.LineCollection(
self._edges(X, Y),
colors=(mpl.rcParams['axes.edgecolor'],),
linewidths=(0.5 * mpl.rcParams['axes.linewidth'],))
self.ax.add_collection(self.dividers)
[docs]def colorbar_factory(cax, mappable, **kwargs):
"""
Creates a colorbar on the given axes for the given mappable.
Typically, for automatic colorbar placement given only a mappable use
:meth:`~matplotlib.figure.Figure.colorbar`.
"""
# if the given mappable is a contourset with any hatching, use
# ColorbarPatch else use Colorbar
if (isinstance(mappable, contour.ContourSet)
and any(hatch is not None for hatch in mappable.hatches)):
cb = ColorbarPatch(cax, mappable, **kwargs)
else:
cb = Colorbar(cax, mappable, **kwargs)
cid = mappable.callbacksSM.connect('changed', cb.on_mappable_changed)
mappable.colorbar = cb
mappable.colorbar_cid = cid
return cb