"""

Use this module directly:

import xarray.plot as xplt

Or use the methods on a DataArray:

DataArray.plot._____

"""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import functools

import warnings

import numpy as np

import pandas as pd

from datetime import datetime

from .utils import (ROBUST_PERCENTILE, _determine_cmap_params,

_infer_xy_labels, get_axis, import_matplotlib_pyplot)

from .facetgrid import FacetGrid

from xarray.core.pycompat import basestring

def _valid_numpy_subdtype(x, numpy_types):

"""

Is any dtype from numpy_types superior to the dtype of x?

"""

# If any of the types given in numpy_types is understood as numpy.generic,

# all possible x will be considered valid. This is probably unwanted.

for t in numpy_types:

assert not np.issubdtype(np.generic, t)

return any(np.issubdtype(x.dtype, t) for t in numpy_types)

def _valid_other_type(x, types):

"""

Do all elements of x have a type from types?

"""

return all(any(isinstance(el, t) for t in types) for el in np.ravel(x))

def _ensure_plottable(*args):

"""

Raise exception if there is anything in args that can't be plotted on an

axis.

"""

numpy_types = [np.floating, np.integer, np.timedelta64, np.datetime64]

other_types = [datetime]

for x in args:

if not (_valid_numpy_subdtype(np.array(x), numpy_types) or

_valid_other_type(np.array(x), other_types)):

raise TypeError('Plotting requires coordinates to be numeric '

'or dates.')

def _easy_facetgrid(darray, plotfunc, x, y, row=None, col=None,

col_wrap=None, sharex=True, sharey=True, aspect=None,

size=None, subplot_kws=None, **kwargs):

"""

Convenience method to call xarray.plot.FacetGrid from 2d plotting methods

kwargs are the arguments to 2d plotting method

"""

ax = kwargs.pop('ax', None)

figsize = kwargs.pop('figsize', None)

if ax is not None:

raise ValueError("Can't use axes when making faceted plots.")

if aspect is None:

aspect = 1

if size is None:

size = 3

elif figsize is not None:

raise ValueError('cannot provide both `figsize` and `size` arguments')

g = FacetGrid(data=darray, col=col, row=row, col_wrap=col_wrap,

sharex=sharex, sharey=sharey, figsize=figsize,

aspect=aspect, size=size, subplot_kws=subplot_kws)

return g.map_dataarray(plotfunc, x, y, **kwargs)

def plot(darray, row=None, col=None, col_wrap=None, ax=None, rtol=0.01,

subplot_kws=None, **kwargs):

"""

Default plot of DataArray using matplotlib.pyplot.

Calls xarray plotting function based on the dimensions of

darray.squeeze()

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

Dimensions Plotting function

--------------- ---------------------------

1 :py:func:`xarray.plot.line`

2 :py:func:`xarray.plot.pcolormesh`

Anything else :py:func:`xarray.plot.hist`

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

Parameters

----------

darray : DataArray

row : string, optional

If passed, make row faceted plots on this dimension name

col : string, optional

If passed, make column faceted plots on this dimension name

col_wrap : integer, optional

Use together with ``col`` to wrap faceted plots

ax : matplotlib axes, optional

If None, uses the current axis. Not applicable when using facets.

rtol : number, optional

Relative tolerance used to determine if the indexes

are uniformly spaced. Usually a small positive number.

subplot_kws : dict, optional

Dictionary of keyword arguments for matplotlib subplots. Only applies

to FacetGrid plotting.

**kwargs : optional

Additional keyword arguments to matplotlib

"""

darray = darray.squeeze()

plot_dims = set(darray.dims)

plot_dims.discard(row)

plot_dims.discard(col)

ndims = len(plot_dims)

error_msg = ('Only 2d plots are supported for facets in xarray. '

'See the package `Seaborn` for more options.')

if ndims == 1:

if row or col:

raise ValueError(error_msg)

plotfunc = line

elif ndims == 2:

# Only 2d can FacetGrid

kwargs['row'] = row

kwargs['col'] = col

kwargs['col_wrap'] = col_wrap

kwargs['subplot_kws'] = subplot_kws

plotfunc = pcolormesh

else:

if row or col:

raise ValueError(error_msg)

plotfunc = hist

kwargs['ax'] = ax

return plotfunc(darray, **kwargs)

# This function signature should not change so that it can use

# matplotlib format strings

def line(darray, *args, **kwargs):

"""

Line plot of DataArray index against values

Wraps :func:`matplotlib:matplotlib.pyplot.plot`

Parameters

----------

darray : DataArray

Must be 1 dimensional

figsize : tuple, optional

A tuple (width, height) of the figure in inches.

Mutually exclusive with ``size`` and ``ax``.

aspect : scalar, optional

Aspect ratio of plot, so that ``aspect * size`` gives the width in

inches. Only used if a ``size`` is provided.

size : scalar, optional

If provided, create a new figure for the plot with the given size.

Height (in inches) of each plot. See also: ``aspect``.

ax : matplotlib axes object, optional

Axis on which to plot this figure. By default, use the current axis.

Mutually exclusive with ``size`` and ``figsize``.

hue : string, optional

Coordinate for which you want multiple lines plotted (2D inputs only).

x : string, optional

Coordinate for x axis.

add_legend : boolean, optional

Add legend with y axis coordinates (2D inputs only).

*args, **kwargs : optional

Additional arguments to matplotlib.pyplot.plot

"""

ndims = len(darray.dims)

if ndims > 2:

raise ValueError('Line plots are for 1- or 2-dimensional DataArrays. '

'Passed DataArray has {ndims} '

'dimensions'.format(ndims=ndims))

# Ensures consistency with .plot method

figsize = kwargs.pop('figsize', None)

aspect = kwargs.pop('aspect', None)

size = kwargs.pop('size', None)

ax = kwargs.pop('ax', None)

hue = kwargs.pop('hue', None)

x = kwargs.pop('x', None)

add_legend = kwargs.pop('add_legend', True)

ax = get_axis(figsize, size, aspect, ax)

if ndims == 1:

xlabel, = darray.dims

if x is not None and xlabel != x:

raise ValueError('Input does not have specified dimension'

' {!r}'.format(x))

x = darray.coords[xlabel]

else:

if x is None and hue is None:

raise ValueError('For 2D inputs, please specify either hue or x.')

xlabel, huelabel = _infer_xy_labels(darray=darray, x=x, y=hue)

x = darray.coords[xlabel]

darray = darray.transpose(xlabel, huelabel)

_ensure_plottable(x)

primitive = ax.plot(x, darray, *args, **kwargs)

ax.set_xlabel(xlabel)

ax.set_title(darray._title_for_slice())

if darray.name is not None:

ax.set_ylabel(darray.name)

if darray.ndim == 2 and add_legend:

ax.legend(handles=primitive,

labels=list(darray.coords[huelabel].values),

title=huelabel)

# Rotate dates on xlabels

if np.issubdtype(x.dtype, np.datetime64):

ax.get_figure().autofmt_xdate()

return primitive

def hist(darray, figsize=None, size=None, aspect=None, ax=None, **kwargs):

"""

Histogram of DataArray

Wraps :func:`matplotlib:matplotlib.pyplot.hist`

Plots N dimensional arrays by first flattening the array.

Parameters

----------

darray : DataArray

Can be any dimension

figsize : tuple, optional

A tuple (width, height) of the figure in inches.

Mutually exclusive with ``size`` and ``ax``.

aspect : scalar, optional

Aspect ratio of plot, so that ``aspect * size`` gives the width in

inches. Only used if a ``size`` is provided.

size : scalar, optional

If provided, create a new figure for the plot with the given size.

Height (in inches) of each plot. See also: ``aspect``.

ax : matplotlib axes object, optional

Axis on which to plot this figure. By default, use the current axis.

Mutually exclusive with ``size`` and ``figsize``.

**kwargs : optional

Additional keyword arguments to matplotlib.pyplot.hist

"""

ax = get_axis(figsize, size, aspect, ax)

no_nan = np.ravel(darray.values)

no_nan = no_nan[pd.notnull(no_nan)]

primitive = ax.hist(no_nan, **kwargs)

ax.set_ylabel('Count')

if darray.name is not None:

ax.set_title('Histogram of {0}'.format(darray.name))

return primitive

def _update_axes_limits(ax, xincrease, yincrease):

"""

Update axes in place to increase or decrease

For use in _plot2d

"""

if xincrease is None:

pass

elif xincrease:

ax.set_xlim(sorted(ax.get_xlim()))

elif not xincrease:

ax.set_xlim(sorted(ax.get_xlim(), reverse=True))

if yincrease is None:

pass

elif yincrease:

ax.set_ylim(sorted(ax.get_ylim()))

elif not yincrease:

ax.set_ylim(sorted(ax.get_ylim(), reverse=True))

# MUST run before any 2d plotting functions are defined since

# _plot2d decorator adds them as methods here.

class _PlotMethods(object):

"""

Enables use of xarray.plot functions as attributes on a DataArray.

For example, DataArray.plot.imshow

"""

def __init__(self, darray):

self._da = darray

def __call__(self, **kwargs):

return plot(self._da, **kwargs)

@functools.wraps(hist)

def hist(self, ax=None, **kwargs):

return hist(self._da, ax=ax, **kwargs)

@functools.wraps(line)

def line(self, *args, **kwargs):

return line(self._da, *args, **kwargs)

def _rescale_imshow_rgb(darray, vmin, vmax, robust):

assert robust or vmin is not None or vmax is not None

# There's a cyclic dependency via DataArray, so we can't import from

# xarray.ufuncs in global scope.

from xarray.ufuncs import maximum, minimum

# Calculate vmin and vmax automatically for `robust=True`

if robust:

if vmax is None:

vmax = np.nanpercentile(darray, 100 - ROBUST_PERCENTILE)

if vmin is None:

vmin = np.nanpercentile(darray, ROBUST_PERCENTILE)

# If not robust and one bound is None, calculate the default other bound

# and check that an interval between them exists.

elif vmax is None:

vmax = 255 if np.issubdtype(darray.dtype, np.integer) else 1

if vmax < vmin:

raise ValueError(

'vmin=%r is less than the default vmax (%r) - you must supply '

'a vmax > vmin in this case.' % (vmin, vmax))

elif vmin is None:

vmin = 0

if vmin > vmax:

raise ValueError(

'vmax=%r is less than the default vmin (0) - you must supply '

'a vmin < vmax in this case.' % vmax)

# Scale interval [vmin .. vmax] to [0 .. 1], with darray as 64-bit float

# to avoid precision loss, integer over/underflow, etc with extreme inputs.

# After scaling, downcast to 32-bit float. This substantially reduces

# memory usage after we hand `darray` off to matplotlib.

darray = ((darray.astype('f8') - vmin) / (vmax - vmin)).astype('f4')

return minimum(maximum(darray, 0), 1)

def _plot2d(plotfunc):

"""

Decorator for common 2d plotting logic

Also adds the 2d plot method to class _PlotMethods

"""

commondoc = """

Parameters

----------

darray : DataArray

Must be 2 dimensional, unless creating faceted plots

x : string, optional

Coordinate for x axis. If None use darray.dims[1]

y : string, optional

Coordinate for y axis. If None use darray.dims[0]

figsize : tuple, optional

A tuple (width, height) of the figure in inches.

Mutually exclusive with ``size`` and ``ax``.

aspect : scalar, optional

Aspect ratio of plot, so that ``aspect * size`` gives the width in

inches. Only used if a ``size`` is provided.

size : scalar, optional

If provided, create a new figure for the plot with the given size.

Height (in inches) of each plot. See also: ``aspect``.

ax : matplotlib axes object, optional

Axis on which to plot this figure. By default, use the current axis.

Mutually exclusive with ``size`` and ``figsize``.

row : string, optional

If passed, make row faceted plots on this dimension name

col : string, optional

If passed, make column faceted plots on this dimension name

col_wrap : integer, optional

Use together with ``col`` to wrap faceted plots

xincrease : None, True, or False, optional

Should the values on the x axes be increasing from left to right?

if None, use the default for the matplotlib function

yincrease : None, True, or False, optional

Should the values on the y axes be increasing from top to bottom?

if None, use the default for the matplotlib function

add_colorbar : Boolean, optional

Adds colorbar to axis

add_labels : Boolean, optional

Use xarray metadata to label axes

vmin, vmax : floats, optional

Values to anchor the colormap, otherwise they are inferred from the

data and other keyword arguments. When a diverging dataset is inferred,

setting one of these values will fix the other by symmetry around

``center``. Setting both values prevents use of a diverging colormap.

If discrete levels are provided as an explicit list, both of these

values are ignored.

cmap : matplotlib colormap name or object, optional

The mapping from data values to color space. If not provided, this

will be either be ``viridis`` (if the function infers a sequential

dataset) or ``RdBu_r`` (if the function infers a diverging dataset).

When `Seaborn` is installed, ``cmap`` may also be a `seaborn`

color palette. If ``cmap`` is seaborn color palette and the plot type

is not ``contour`` or ``contourf``, ``levels`` must also be specified.

colors : discrete colors to plot, optional

A single color or a list of colors. If the plot type is not ``contour``

or ``contourf``, the ``levels`` argument is required.

center : float, optional

The value at which to center the colormap. Passing this value implies

use of a diverging colormap. Setting it to ``False`` prevents use of a

diverging colormap.

robust : bool, optional

If True and ``vmin`` or ``vmax`` are absent, the colormap range is

computed with 2nd and 98th percentiles instead of the extreme values.

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

How to draw arrows extending the colorbar beyond its limits. If not

provided, extend is inferred from vmin, vmax and the data limits.

levels : int or list-like object, optional

Split the colormap (cmap) into discrete color intervals. If an integer

is provided, "nice" levels are chosen based on the data range: this can

imply that the final number of levels is not exactly the expected one.

Setting ``vmin`` and/or ``vmax`` with ``levels=N`` is equivalent to

setting ``levels=np.linspace(vmin, vmax, N)``.

infer_intervals : bool, optional

Only applies to pcolormesh. If True, the coordinate intervals are

passed to pcolormesh. If False, the original coordinates are used

(this can be useful for certain map projections). The default is to

always infer intervals, unless the mesh is irregular and plotted on

a map projection.

subplot_kws : dict, optional

Dictionary of keyword arguments for matplotlib subplots. Only applies

to FacetGrid plotting.

cbar_ax : matplotlib Axes, optional

Axes in which to draw the colorbar.

cbar_kwargs : dict, optional

Dictionary of keyword arguments to pass to the colorbar.

**kwargs : optional

Additional arguments to wrapped matplotlib function

Returns

-------

artist :

The same type of primitive artist that the wrapped matplotlib

function returns

"""

# Build on the original docstring

plotfunc.__doc__ = '%s\n%s' % (plotfunc.__doc__, commondoc)

@functools.wraps(plotfunc)

def newplotfunc(darray, x=None, y=None, figsize=None, size=None,

aspect=None, ax=None, row=None, col=None,

col_wrap=None, xincrease=True, yincrease=True,

add_colorbar=None, add_labels=True, vmin=None, vmax=None,

cmap=None, center=None, robust=False, extend=None,

levels=None, infer_intervals=None, colors=None,

subplot_kws=None, cbar_ax=None, cbar_kwargs=None,

**kwargs):

# All 2d plots in xarray share this function signature.

# Method signature below should be consistent.

# Decide on a default for the colorbar before facetgrids

if add_colorbar is None:

add_colorbar = plotfunc.__name__ != 'contour'

imshow_rgb = (

plotfunc.__name__ == 'imshow' and

darray.ndim == (3 + (row is not None) + (col is not None)))

if imshow_rgb:

# Don't add a colorbar when showing an image with explicit colors

add_colorbar = False

# Matplotlib does not support normalising RGB data, so do it here.

# See eg. https://github.com/matplotlib/matplotlib/pull/10220

if robust or vmax is not None or vmin is not None:

darray = _rescale_imshow_rgb(darray, vmin, vmax, robust)

vmin, vmax, robust = None, None, False

# Handle facetgrids first

if row or col:

allargs = locals().copy()

allargs.pop('imshow_rgb')

allargs.update(allargs.pop('kwargs'))

# Need the decorated plotting function

allargs['plotfunc'] = globals()[plotfunc.__name__]

return _easy_facetgrid(**allargs)

plt = import_matplotlib_pyplot()

# colors is mutually exclusive with cmap

if cmap and colors:

raise ValueError("Can't specify both cmap and colors.")

# colors is only valid when levels is supplied or the plot is of type

# contour or contourf

if colors and (('contour' not in plotfunc.__name__) and (not levels)):

raise ValueError("Can only specify colors with contour or levels")

# we should not be getting a list of colors in cmap anymore

# is there a better way to do this test?

if isinstance(cmap, (list, tuple)):

warnings.warn("Specifying a list of colors in cmap is deprecated. "

"Use colors keyword instead.",

DeprecationWarning, stacklevel=3)

rgb = kwargs.pop('rgb', None)

xlab, ylab = _infer_xy_labels(

darray=darray, x=x, y=y, imshow=imshow_rgb, rgb=rgb)

if rgb is not None and plotfunc.__name__ != 'imshow':

raise ValueError('The "rgb" keyword is only valid for imshow()')

elif rgb is not None and not imshow_rgb:

raise ValueError('The "rgb" keyword is only valid for imshow()'

'with a three-dimensional array (per facet)')

# better to pass the ndarrays directly to plotting functions

xval = darray[xlab].values

yval = darray[ylab].values

# check if we need to broadcast one dimension

if xval.ndim < yval.ndim:

xval = np.broadcast_to(xval, yval.shape)

if yval.ndim < xval.ndim:

yval = np.broadcast_to(yval, xval.shape)

# May need to transpose for correct x, y labels

# xlab may be the name of a coord, we have to check for dim names

if imshow_rgb:

# For RGB[A] images, matplotlib requires the color dimension

# to be last. In Xarray the order should be unimportant, so

# we transpose to (y, x, color) to make this work.

yx_dims = (ylab, xlab)

dims = yx_dims + tuple(d for d in darray.dims if d not in yx_dims)

if dims != darray.dims:

darray = darray.transpose(*dims)

elif darray[xlab].dims[-1] == darray.dims[0]:

darray = darray.transpose()

# Pass the data as a masked ndarray too

zval = darray.to_masked_array(copy=False)

_ensure_plottable(xval, yval)

if 'contour' in plotfunc.__name__ and levels is None:

levels = 7 # this is the matplotlib default

cmap_kwargs = {'plot_data': zval.data,

'vmin': vmin,

'vmax': vmax,

'cmap': colors if colors else cmap,

'center': center,

'robust': robust,

'extend': extend,

'levels': levels,

'filled': plotfunc.__name__ != 'contour',

}

cmap_params = _determine_cmap_params(**cmap_kwargs)

if 'contour' in plotfunc.__name__:

# extend is a keyword argument only for contour and contourf, but

# passing it to the colorbar is sufficient for imshow and

# pcolormesh

kwargs['extend'] = cmap_params['extend']

kwargs['levels'] = cmap_params['levels']

if 'pcolormesh' == plotfunc.__name__:

kwargs['infer_intervals'] = infer_intervals

# This allows the user to pass in a custom norm coming via kwargs

kwargs.setdefault('norm', cmap_params['norm'])

if 'imshow' == plotfunc.__name__ and isinstance(aspect, basestring):

# forbid usage of mpl strings

raise ValueError("plt.imshow's `aspect` kwarg is not available "

"in xarray")

ax = get_axis(figsize, size, aspect, ax)

primitive = plotfunc(xval, yval, zval, ax=ax, cmap=cmap_params['cmap'],

vmin=cmap_params['vmin'],

vmax=cmap_params['vmax'],

**kwargs)

# Label the plot with metadata

if add_labels:

ax.set_xlabel(xlab)

ax.set_ylabel(ylab)

ax.set_title(darray._title_for_slice())

if add_colorbar:

cbar_kwargs = {} if cbar_kwargs is None else dict(cbar_kwargs)

cbar_kwargs.setdefault('extend', cmap_params['extend'])

if cbar_ax is None:

cbar_kwargs.setdefault('ax', ax)

else:

cbar_kwargs.setdefault('cax', cbar_ax)

cbar = plt.colorbar(primitive, **cbar_kwargs)

if darray.name and add_labels and 'label' not in cbar_kwargs:

cbar.set_label(darray.name, rotation=90)

elif cbar_ax is not None or cbar_kwargs is not None:

# inform the user about keywords which aren't used

raise ValueError("cbar_ax and cbar_kwargs can't be used with "

"add_colorbar=False.")

_update_axes_limits(ax, xincrease, yincrease)

# Rotate dates on xlabels

if np.issubdtype(xval.dtype, np.datetime64):

ax.get_figure().autofmt_xdate()

return primitive

# For use as DataArray.plot.plotmethod

@functools.wraps(newplotfunc)

def plotmethod(_PlotMethods_obj, x=None, y=None, figsize=None, size=None,

aspect=None, ax=None, row=None, col=None, col_wrap=None,

xincrease=True, yincrease=True, add_colorbar=None,

add_labels=True, vmin=None, vmax=None, cmap=None,

colors=None, center=None, robust=False, extend=None,

levels=None, infer_intervals=None, subplot_kws=None,

cbar_ax=None, cbar_kwargs=None, **kwargs):

"""

The method should have the same signature as the function.

This just makes the method work on Plotmethods objects,

and passes all the other arguments straight through.

"""

allargs = locals()

allargs['darray'] = _PlotMethods_obj._da

allargs.update(kwargs)

for arg in ['_PlotMethods_obj', 'newplotfunc', 'kwargs']:

del allargs[arg]

return newplotfunc(**allargs)

# Add to class _PlotMethods

setattr(_PlotMethods, plotmethod.__name__, plotmethod)

return newplotfunc

@_plot2d

def imshow(x, y, z, ax, **kwargs):

"""

Image plot of 2d DataArray using matplotlib.pyplot

Wraps :func:`matplotlib:matplotlib.pyplot.imshow`

While other plot methods require the DataArray to be strictly

two-dimensional, ``imshow`` also accepts a 3D array where some

dimension can be interpreted as RGB or RGBA color channels and

allows this dimension to be specified via the kwarg ``rgb=``.

Unlike matplotlib, Xarray can apply ``vmin`` and ``vmax`` to RGB or RGBA

data, by applying a single scaling factor and offset to all bands.

Passing ``robust=True`` infers ``vmin`` and ``vmax``

:ref:`in the usual way <robust-plotting>`.

.. note::

This function needs uniformly spaced coordinates to

properly label the axes. Call DataArray.plot() to check.

The pixels are centered on the coordinates values. Ie, if the coordinate

value is 3.2 then the pixels for those coordinates will be centered on 3.2.

"""

if x.ndim != 1 or y.ndim != 1:

raise ValueError('imshow requires 1D coordinates, try using '

'pcolormesh or contour(f)')

# Centering the pixels- Assumes uniform spacing

try:

xstep = (x[1] - x[0]) / 2.0

except IndexError:

# Arbitrary default value, similar to matplotlib behaviour

xstep = .1

try:

ystep = (y[1] - y[0]) / 2.0

except IndexError:

ystep = .1

left, right = x[0] - xstep, x[-1] + xstep

bottom, top = y[-1] + ystep, y[0] - ystep

defaults = {'extent': [left, right, bottom, top],

'origin': 'upper',

'interpolation': 'nearest',

}

if not hasattr(ax, 'projection'):

# not for cartopy geoaxes

defaults['aspect'] = 'auto'

# Allow user to override these defaults

defaults.update(kwargs)

if z.ndim == 3:

# matplotlib imshow uses black for missing data, but Xarray makes

# missing data transparent. We therefore add an alpha channel if

# there isn't one, and set it to transparent where data is masked.

if z.shape[-1] == 3:

z = np.ma.concatenate((z, np.ma.ones(z.shape[:2] + (1,))), 2)

z = z.copy()

z[np.any(z.mask, axis=-1), -1] = 0

primitive = ax.imshow(z, **defaults)

return primitive

@_plot2d

def contour(x, y, z, ax, **kwargs):

"""

Contour plot of 2d DataArray

Wraps :func:`matplotlib:matplotlib.pyplot.contour`

"""

primitive = ax.contour(x, y, z, **kwargs)

return primitive

@_plot2d

def contourf(x, y, z, ax, **kwargs):

"""

Filled contour plot of 2d DataArray

Wraps :func:`matplotlib:matplotlib.pyplot.contourf`

"""

primitive = ax.contourf(x, y, z, **kwargs)

return primitive

def _infer_interval_breaks(coord, axis=0):

"""

>>> _infer_interval_breaks(np.arange(5))

array([-0.5, 0.5, 1.5, 2.5, 3.5, 4.5])

>>> _infer_interval_breaks([[0, 1], [3, 4]], axis=1)

array([[-0.5, 0.5, 1.5],

[ 2.5, 3.5, 4.5]])

"""

coord = np.asarray(coord)

deltas = 0.5 * np.diff(coord, axis=axis)

if deltas.size == 0:

deltas = np.array(0.0)

first = np.take(coord, [0], axis=axis) - np.take(deltas, [0], axis=axis)

last = np.take(coord, [-1], axis=axis) + np.take(deltas, [-1], axis=axis)

trim_last = tuple(slice(None, -1) if n == axis else slice(None)

for n in range(coord.ndim))

return np.concatenate([first, coord[trim_last] + deltas, last], axis=axis)

@_plot2d

def pcolormesh(x, y, z, ax, infer_intervals=None, **kwargs):

"""

Pseudocolor plot of 2d DataArray

Wraps :func:`matplotlib:matplotlib.pyplot.pcolormesh`

"""

# decide on a default for infer_intervals (GH781)

x = np.asarray(x)

if infer_intervals is None:

if hasattr(ax, 'projection'):

if len(x.shape) == 1:

infer_intervals = True

else:

infer_intervals = False

else:

infer_intervals = True

if infer_intervals:

if len(x.shape) == 1:

x = _infer_interval_breaks(x)

y = _infer_interval_breaks(y)

else:

# we have to infer the intervals on both axes

x = _infer_interval_breaks(x, axis=1)

x = _infer_interval_breaks(x, axis=0)

y = _infer_interval_breaks(y, axis=1)

y = _infer_interval_breaks(y, axis=0)

primitive = ax.pcolormesh(x, y, z, **kwargs)

# by default, pcolormesh picks "round" values for bounds

# this results in ugly looking plots with lots of surrounding whitespace

if not hasattr(ax, 'projection') and x.ndim == 1 and y.ndim == 1:

# not a cartopy geoaxis

ax.set_xlim(x[0], x[-1])

ax.set_ylim(y[0], y[-1])

return primitive