/usr/lib/python3/dist-packages/xarray/plot/plot.py is in python3-xarray 0.10.2-1.
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Use this module directly:
import xarray.plot as xplt
Or use the methods on a DataArray:
DataArray.plot._____
"""
from __future__ import absolute_import, division, print_function
import functools
import warnings
from datetime import datetime
import numpy as np
import pandas as pd
from xarray.core.pycompat import basestring
from .facetgrid import FacetGrid
from .utils import (
ROBUST_PERCENTILE, _determine_cmap_params, _infer_xy_labels, get_axis,
import_matplotlib_pyplot)
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 DataArrays only).
x, y : string, optional
Coordinates for x, y axis. Only one of these may be specified.
The other coordinate plots values from the DataArray on which this
plot method is called.
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)
y = kwargs.pop('y', None)
add_legend = kwargs.pop('add_legend', True)
ax = get_axis(figsize, size, aspect, ax)
error_msg = ('must be either None or one of ({0:s})'
.format(', '.join([repr(dd) for dd in darray.dims])))
if x is not None and x not in darray.dims:
raise ValueError('x ' + error_msg)
if y is not None and y not in darray.dims:
raise ValueError('y ' + error_msg)
if x is not None and y is not None:
raise ValueError('You cannot specify both x and y kwargs'
'for line plots.')
if ndims == 1:
dim, = darray.dims # get the only dimension name
if (x is None and y is None) or x == dim:
xplt = darray.coords[dim]
yplt = darray
xlabel = dim
ylabel = darray.name
else:
yplt = darray.coords[dim]
xplt = darray
xlabel = darray.name
ylabel = dim
else:
if x is None and y is None and hue is None:
raise ValueError('For 2D inputs, please specify either hue or x.')
if y is None:
xlabel, huelabel = _infer_xy_labels(darray=darray, x=x, y=hue)
ylabel = darray.name
xplt = darray.coords[xlabel]
yplt = darray.transpose(xlabel, huelabel)
else:
ylabel, huelabel = _infer_xy_labels(darray=darray, x=y, y=hue)
xlabel = darray.name
xplt = darray.transpose(ylabel, huelabel)
yplt = darray.coords[ylabel]
_ensure_plottable(xplt)
primitive = ax.plot(xplt, yplt, *args, **kwargs)
if xlabel is not None:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
ax.set_title(darray._title_for_slice())
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(xplt.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:
alpha = np.ma.ones(z.shape[:2] + (1,), dtype=z.dtype)
if np.issubdtype(z.dtype, np.integer):
alpha *= 255
z = np.ma.concatenate((z, alpha), axis=2)
else:
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 _is_monotonic(coord, axis=0):
"""
>>> _is_monotonic(np.array([0, 1, 2]))
True
>>> _is_monotonic(np.array([2, 1, 0]))
True
>>> _is_monotonic(np.array([0, 2, 1]))
False
"""
if coord.shape[axis] < 3:
return True
else:
n = coord.shape[axis]
delta_pos = (coord.take(np.arange(1, n), axis=axis) >=
coord.take(np.arange(0, n - 1), axis=axis))
delta_neg = (coord.take(np.arange(1, n), axis=axis) <=
coord.take(np.arange(0, n - 1), axis=axis))
return np.all(delta_pos) or np.all(delta_neg)
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)
if not _is_monotonic(coord, axis=axis):
raise ValueError("The input coordinate is not sorted in increasing "
"order along axis %d. This can lead to unexpected "
"results. Consider calling the `sortby` method on "
"the input DataArray. To plot data with categorical "
"axes, consider using the `heatmap` function from "
"the `seaborn` statistical plotting library." % axis)
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
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