"""

Colorbars are a visualization of the mapping from scalar values to colors.

In Matplotlib they are drawn into a dedicated `~.axes.Axes`.

.. note::

Colorbars are typically created through `.Figure.colorbar` or its pyplot

wrapper `.pyplot.colorbar`, which use `.make_axes` and `.Colorbar`

internally.

As an end-user, you most likely won't have to call the methods or

instantiate the classes in this module explicitly.

: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`

On top of `.ColorbarBase` this connects the colorbar with a

`.ScalarMappable` such as an image or contour plot.

:func:`make_axes`

Create an `~.axes.Axes` suitable for a colorbar. This functions can be

used with figures containing a single axes or with freely placed axes.

:func:`make_axes_gridspec`

Create a `~.SubplotBase` suitable for a colorbar. This function should

be used for adding a colorbar to a `.GridSpec`.

"""

import copy

import logging

import textwrap

import numpy as np

import matplotlib as mpl

from matplotlib import _api, collections, cm, colors, contour, ticker

import matplotlib.artist as martist

import matplotlib.patches as mpatches

import matplotlib.path as mpath

import matplotlib.spines as mspines

import matplotlib.transforms as mtransforms

from matplotlib import docstring

_log = logging.getLogger(__name__)

_make_axes_param_doc = """

location : None or {'left', 'right', 'top', 'bottom'}

The location, relative to the parent axes, where the colorbar axes

is created. It also determines the *orientation* of the colorbar

(colorbars on the left and right are vertical, colorbars at the top

and bottom are horizontal). If None, the location will come from the

*orientation* if it is set (vertical colorbars on the right, horizontal

ones at the bottom), or default to 'right' if *orientation* is unset.

orientation : None or {'vertical', 'horizontal'}

The orientation of the colorbar. It is preferable to set the *location*

of the colorbar, as that also determines the *orientation*; passing

incompatible values for *location* and *orientation* raises an exception.

fraction : float, default: 0.15

Fraction of original axes to use for colorbar.

shrink : float, default: 1.0

Fraction by which to multiply the size of the colorbar.

aspect : float, default: 20

Ratio of long to short dimensions.

"""

_make_axes_other_param_doc = """

pad : float, default: 0.05 if vertical, 0.15 if horizontal

Fraction of original axes between colorbar and new image axes.

anchor : (float, float), optional

The anchor point of the colorbar axes.

Defaults to (0.0, 0.5) if vertical; (0.5, 1.0) if horizontal.

panchor : (float, float), or *False*, optional

The anchor point of the colorbar parent axes. If *False*, the parent

axes' anchor will be unchanged.

Defaults to (1.0, 0.5) if vertical; (0.5, 0.0) if horizontal.

"""

_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* or list of ticks or Locator

If None, ticks are determined automatically from the

input.

*format* None or str or Formatter

If None, `~.ticker.ScalarFormatter` is used.

If a format string is given, e.g., '%.3f', that is used.

An alternative `~.ticker.Formatter` may be given instead.

*drawedges* bool

Whether to draw lines at color boundaries.

*label* str

The label on the colorbar's long axis.

============ ====================================================

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

colormapped to the corresponding value in values

will be used.

============ ===================================================

"""

docstring.interpd.update(colorbar_doc="""

Add a colorbar to a plot.

Parameters

----------

mappable

The `matplotlib.cm.ScalarMappable` (i.e., `~matplotlib.image.AxesImage`,

`~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 : `~matplotlib.axes.Axes`, optional

Axes into which the colorbar will be drawn.

ax : `~matplotlib.axes.Axes`, list of Axes, optional

One or more parent axes from which space for a new colorbar axes will be

stolen, if *cax* is None. This has no effect if *cax* is set.

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`.

Notes

-----

Additional keyword arguments are of two kinds:

axes properties:

%s

%s

colorbar properties:

%s

If *mappable* is a `~.contour.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).

""" % (textwrap.indent(_make_axes_param_doc, " "),

textwrap.indent(_make_axes_other_param_doc, " "),

_colormap_kw_doc))

# Deprecated since 3.4.

colorbar_doc = docstring.interpd.params["colorbar_doc"]

colormap_kw_doc = _colormap_kw_doc

make_axes_kw_doc = _make_axes_param_doc + _make_axes_other_param_doc

def _set_ticks_on_axis_warn(*args, **kw):

# a top level function which gets put in at the axes'

# set_xticks and set_yticks by ColorbarBase.__init__.

_api.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):

"""

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 _ColorbarSpine(mspines.Spine):

def __init__(self, axes):

super().__init__(axes, 'colorbar',

mpath.Path(np.empty((0, 2)), closed=True))

def get_window_extent(self, renderer=None):

# This Spine has no Axis associated with it, and doesn't need to adjust

# its location, so we can directly get the window extent from the

# super-super-class.

return mpatches.Patch.get_window_extent(self, renderer=renderer)

def set_xy(self, xy):

self._path = mpath.Path(xy, closed=True)

self.stale = True

def draw(self, renderer):

ret = mpatches.Patch.draw(self, renderer)

self.stale = False

return ret

class ColorbarBase:

r"""

Draw a colorbar in an existing axes.

There are only some rare cases in which you would work directly with a

`.ColorbarBase` as an end-user. Typically, colorbars are used

with `.ScalarMappable`\s such as an `.AxesImage` generated via

`~.axes.Axes.imshow`. For these cases you will use `.Colorbar` and

likely create it via `.pyplot.colorbar` or `.Figure.colorbar`.

The main application of using a `.ColorbarBase` explicitly is drawing

colorbars that are not associated with other elements in the figure, e.g.

when showing a colormap by itself.

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::

norm=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 : `~matplotlib.axes.Axes`

The `~.axes.Axes` instance in which the colorbar is drawn.

lines : list

A list of `.LineCollection` (empty if no lines were drawn).

dividers : `.LineCollection`

A LineCollection (empty if *drawedges* is ``False``).

Parameters

----------

ax : `~matplotlib.axes.Axes`

The `~.axes.Axes` instance in which the colorbar is drawn.

cmap : `~matplotlib.colors.Colormap`, default: :rc:`image.cmap`

The colormap to use.

norm : `~matplotlib.colors.Normalize`

alpha : float

The colorbar transparency between 0 (transparent) and 1 (opaque).

values

boundaries

orientation : {'vertical', 'horizontal'}

ticklocation : {'auto', 'left', 'right', 'top', 'bottom'}

extend : {'neither', 'both', 'min', 'max'}

spacing : {'uniform', 'proportional'}

ticks : `~matplotlib.ticker.Locator` or array-like of float

format : str or `~matplotlib.ticker.Formatter`

drawedges : bool

filled : bool

extendfrac

extendrec

label : str

"""

n_rasterize = 50 # rasterize solids if number of colors >= n_rasterize

@_api.make_keyword_only("3.3", "cmap")

def __init__(self, ax, cmap=None,

norm=None,

alpha=None,

values=None,

boundaries=None,

orientation='vertical',

ticklocation='auto',

extend=None,

spacing='uniform', # uniform or proportional

ticks=None,

format=None,

drawedges=False,

filled=True,

extendfrac=None,

extendrect=False,

label='',

):

_api.check_isinstance([colors.Colormap, None], cmap=cmap)

_api.check_in_list(

['vertical', 'horizontal'], orientation=orientation)

_api.check_in_list(

['auto', 'left', 'right', 'top', 'bottom'],

ticklocation=ticklocation)

_api.check_in_list(

['uniform', 'proportional'], spacing=spacing)

self.ax = ax

# Bind some methods to the axes to warn users against using them.

ax.set_xticks = ax.set_yticks = _set_ticks_on_axis_warn

ax.set(navigate=False)

if cmap is None:

cmap = cm.get_cmap()

if norm is None:

norm = colors.Normalize()

if extend is None:

if hasattr(norm, 'extend'):

extend = norm.extend

else:

extend = 'neither'

self.alpha = alpha

self.cmap = cmap

self.norm = norm

self.values = values

self.boundaries = boundaries

self.extend = extend

self._inside = _api.check_getitem(

{'neither': slice(0, None), 'both': slice(1, -1),

'min': slice(1, None), 'max': slice(0, -1)},

extend=extend)

self.spacing = spacing

self.orientation = orientation

self.drawedges = drawedges

self.filled = filled

self.extendfrac = extendfrac

self.extendrect = extendrect

self.solids = None

self.solids_patches = []

self.lines = []

for spine in ax.spines.values():

spine.set_visible(False)

self.outline = ax.spines['outline'] = _ColorbarSpine(ax)

self.patch = mpatches.Polygon(

np.empty((0, 2)),

color=mpl.rcParams['axes.facecolor'], linewidth=0.01, zorder=-1)

ax.add_artist(self.patch)

self.dividers = collections.LineCollection(

[],

colors=[mpl.rcParams['axes.edgecolor']],

linewidths=[0.5 * mpl.rcParams['axes.linewidth']])

self.ax.add_collection(self.dividers)

self.locator = None

self.formatter = None

self._manual_tick_data_values = None

self.__scale = None # linear, log10 for now. Hopefully more?

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 upper limit is open ended."""

return self.extend in ('both', 'max')

def draw_all(self):

"""

Calculate any free parameters based on the current cmap and norm,

and do all the drawing.

"""

self._config_axis() # Inline it after deprecation elapses.

# Set self._boundaries and self._values, including extensions.

self._process_values()

# Set self.vmin and self.vmax to first and last boundary, excluding

# extensions.

self.vmin, self.vmax = self._boundaries[self._inside][[0, -1]]

# Compute the X/Y mesh.

X, Y = self._mesh()

# Extract bounding polygon (the last entry's value (X[0, 1]) doesn't

# matter, it just matches the CLOSEPOLY code).

x = np.concatenate([X[[0, 1, -2, -1], 0], X[[-1, -2, 1, 0, 0], 1]])

y = np.concatenate([Y[[0, 1, -2, -1], 0], Y[[-1, -2, 1, 0, 0], 1]])

xy = np.column_stack([x, y])

# Configure axes limits, patch, and outline.

xmin, ymin = xy.min(axis=0)

xmax, ymax = xy.max(axis=0)

self.ax.set(xlim=(xmin, xmax), ylim=(ymin, ymax))

self.outline.set_xy(xy)

self.patch.set_xy(xy)

self.update_ticks()

if self.filled:

self._add_solids(X, Y, self._values[:, np.newaxis])

@_api.deprecated("3.3")

def config_axis(self):

self._config_axis()

def _config_axis(self):

"""Set up long and short axis."""

ax = self.ax

if self.orientation == 'vertical':

long_axis, short_axis = ax.yaxis, ax.xaxis

if mpl.rcParams['ytick.minor.visible']:

self.minorticks_on()

else:

long_axis, short_axis = ax.xaxis, ax.yaxis

if mpl.rcParams['xtick.minor.visible']:

self.minorticks_on()

long_axis.set(label_position=self.ticklocation,

ticks_position=self.ticklocation)

short_axis.set_ticks([])

short_axis.set_ticks([], minor=True)

self.stale = True

def _get_ticker_locator_formatter(self):

"""

Return the ``locator`` and ``formatter`` of the colorbar.

If they have not been defined (i.e. are *None*), suitable formatter

and locator instances will be created, attached to the respective

attributes and returned.

"""

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...)

"""

contouring = self.boundaries is not None and self.spacing == 'uniform'

return (type(self.norm) in [colors.Normalize, colors.LogNorm] and

not contouring)

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):

# *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()

self.__scale = 'log'

else:

self.ax.set_xscale('linear')

self.ax.set_yscale('linear')

if type(self.norm) is colors.Normalize:

self.__scale = 'linear'

else:

self.__scale = 'manual'

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()

long_axis = ax.yaxis if self.orientation == 'vertical' else ax.xaxis

if self._use_auto_colorbar_locator():

_log.debug('Using auto colorbar locator %r on colorbar', 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)

def set_ticks(self, ticks, update_ticks=True):

"""

Set tick locations.

Parameters

----------

ticks : array-like or `~matplotlib.ticker.Locator` or None

The tick positions can be hard-coded by an array of values; or

they can be defined by a `.Locator`. Setting to *None* reverts

to using a default locator.

update_ticks : bool, default: True

If True, tick locations are updated immediately. If False, the

user has to call `update_ticks` later to 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

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

long_axis = (

ax.yaxis if self.orientation == 'vertical' else ax.xaxis)

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

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:

_api.warn_external("set_ticks() must have been called.")

self.stale = True

def minorticks_on(self):

"""

Turn the minor ticks of the colorbar on 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))

def minorticks_off(self):

"""Turn the minor ticks of the colorbar off."""

ax = self.ax

long_axis = ax.yaxis if self.orientation == 'vertical' else ax.xaxis

long_axis.set_minor_locator(ticker.NullLocator())

def set_label(self, label, *, loc=None, **kwargs):

"""

Add a label to the long axis of the colorbar.

Parameters

----------

label : str

The label text.

loc : str, optional

The location of the label.

- For horizontal orientation one of {'left', 'center', 'right'}

- For vertical orientation one of {'bottom', 'center', 'top'}

Defaults to :rc:`xaxis.labellocation` or :rc:`yaxis.labellocation`

depending on the orientation.

**kwargs

Keyword arguments are passed to `~.Axes.set_xlabel` /

`~.Axes.set_ylabel`.

Supported keywords are *labelpad* and `.Text` properties.

"""

if self.orientation == "vertical":

self.ax.set_ylabel(label, loc=loc, **kwargs)

else:

self.ax.set_xlabel(label, loc=loc, **kwargs)

self.stale = True

def _add_solids(self, X, Y, C):

"""Draw the colors; optionally add separators."""

# Cleanup previously set artists.

if self.solids is not None:

self.solids.remove()

for solid in self.solids_patches:

solid.remove()

# Add new artist(s), based on mappable type. Use individual patches if

# hatching is needed, pcolormesh otherwise.

mappable = getattr(self, 'mappable', None)

if (isinstance(mappable, contour.ContourSet)

and any(hatch is not None for hatch in mappable.hatches)):

self._add_solids_patches(X, Y, C, mappable)

else:

self._add_solids_pcolormesh(X, Y, C)

self.dividers.set_segments(

np.dstack([X, Y])[1:-1] if self.drawedges else [])

def _add_solids_pcolormesh(self, X, Y, C):

_log.debug('Setting pcolormesh')

if C.shape[0] == Y.shape[0]:

# trim the last one to be compatible with old behavior.

C = C[:-1]

self.solids = self.ax.pcolormesh(

X, Y, C, cmap=self.cmap, norm=self.norm, alpha=self.alpha,

edgecolors='none', shading='flat')

if not self.drawedges:

if len(self._y) >= self.n_rasterize:

self.solids.set_rasterized(True)

def _add_solids_patches(self, X, Y, C, mappable):

hatches = mappable.hatches * len(C) # Have enough hatches.

patches = []

for i in range(len(X) - 1):

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]]])

patch = mpatches.PathPatch(mpath.Path(xy),

facecolor=self.cmap(self.norm(C[i][0])),

hatch=hatches[i], linewidth=0,

antialiased=False, alpha=self.alpha)

self.ax.add_patch(patch)

patches.append(patch)

self.solids_patches = patches

def add_lines(self, levels, colors, linewidths, erase=True):

"""

Draw lines on the colorbar.

The lines are appended to the list :attr:`lines`.

Parameters

----------

levels : array-like

The positions of the lines.

colors : color or list of colors

Either a single color applying to all lines or one color value for

each line.

linewidths : float or array-like

Either a single linewidth applying to all lines or one linewidth

for each line.

erase : bool, default: True

Whether to remove 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 _get_extension_lengths(self, frac, automin, automax, default=0.05):

"""

Return the lengths of colorbar extensions.

This is a helper method for _uniform_y and _proportional_y.

"""

# Set the default value.

extendlength = np.array([default, default])

if isinstance(frac, str):

_api.check_in_list(['auto'], extendfrac=frac.lower())

# Use the provided values when 'auto' is required.

extendlength[:] = [automin, automax]

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) as err:

# Raise an error on encountering an invalid value for frac.

raise ValueError('invalid value for extendfrac') from err