'''

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 copy

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* or list of ticks or Locator

If None, ticks are determined automatically from the

input.

*format* None or str or Formatter

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.

*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

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`.

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:

"""

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

class ColorbarBase(_ColorbarMappableDummy):

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` if lines were drawn, otherwise

an empty list.

dividers : `.LineCollection`

A LineCollection if *drawedges* is ``True``, otherwise ``None``.

Parameters

----------

ax : `~matplotlib.axes.Axes`

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

cmap : `~matplotlib.colors.Colormap` or None

The colormap to use. If *None*, use :rc:`image.cmap`.

norm : `~matplotlib.colors.Normalize`

alpha : float

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

"""

_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='',

):

cbook._check_isinstance([colors.Colormap, None], cmap=cmap)

cbook._check_in_list(

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

cbook._check_in_list(

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

ticklocation=ticklocation)

cbook._check_in_list(

self._slice_dict, extend=extend)

cbook._check_in_list(

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

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

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 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

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]

# decide minor/major axis

self.config_axis()

self._config_axes(X, Y)

if self.filled:

self._add_solids(X, Y, C)

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...)

"""

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 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)

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

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:

cbook._warn_external("set_ticks() must have been called.")

self.stale = True

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))

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

def set_label(self, label, **kw):

"""Label the long axis of the colorbar."""

self._label = 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)

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."""