"""
Use this module directly:
import xarray.plot as xplt
Or use the methods on a DataArray:
DataArray.plot._____
"""
from __future__ import division
import functools
import warnings
import numpy as np
import pandas as pd
from .utils import _determine_cmap_params, _infer_xy_labels
from .facetgrid import FacetGrid
# Maybe more appropriate to keep this in .utils
def _right_dtype(arr, types):
"""
Is the numpy array a sub dtype of anything in types?
"""
return any(np.issubdtype(arr.dtype, t) for t in types)
def _ensure_plottable(*args):
"""
Raise exception if there is anything in args that can't be plotted on
an axis.
"""
plottypes = [np.floating, np.integer, np.timedelta64, np.datetime64]
# Lists need to be converted to np.arrays here.
if not any(_right_dtype(np.array(x), plottypes) for x in args):
raise TypeError('Plotting requires coordinates to be numeric '
'or dates.')
def _easy_facetgrid(darray, plotfunc, x, y, row=None, col=None, col_wrap=None,
aspect=1, size=3, 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)
if ax is not None:
raise ValueError("Can't use axes when making faceted plots.")
g = FacetGrid(data=darray, col=col, row=row, col_wrap=col_wrap,
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 1 dimensional DataArray index against values
Wraps matplotlib.pyplot.plot
Parameters
----------
darray : DataArray
Must be 1 dimensional
ax : matplotlib axes, optional
If not passed, uses the current axis
*args, **kwargs : optional
Additional arguments to matplotlib.pyplot.plot
"""
import matplotlib.pyplot as plt
ndims = len(darray.dims)
if ndims != 1:
raise ValueError('Line plots are for 1 dimensional DataArrays. '
'Passed DataArray has {ndims} '
'dimensions'.format(ndims=ndims))
# Ensures consistency with .plot method
ax = kwargs.pop('ax', None)
if ax is None:
ax = plt.gca()
xlabel, x = list(darray.indexes.items())[0]
_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)
# Rotate dates on xlabels
if np.issubdtype(x.dtype, np.datetime64):
plt.gcf().autofmt_xdate()
return primitive
def hist(darray, ax=None, **kwargs):
"""
Histogram of DataArray
Wraps matplotlib.pyplot.hist
Plots N dimensional arrays by first flattening the array.
Parameters
----------
darray : DataArray
Can be any dimension
ax : matplotlib axes, optional
If not passed, uses the current axis
**kwargs : optional
Additional keyword arguments to matplotlib.pyplot.hist
"""
import matplotlib.pyplot as plt
if ax is None:
ax = plt.gca()
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 _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]
ax : matplotlib axes object, optional
If None, uses the current axis
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 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.
subplot_kws : dict, optional
Dictionary of keyword arguments for matplotlib subplots. Only applies
to FacetGrid plotting.
**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__ = '\n'.join((plotfunc.__doc__, commondoc))
@functools.wraps(plotfunc)
def newplotfunc(darray, x=None, y=None, ax=None, row=None, col=None,
col_wrap=None, xincrease=True, yincrease=True,
add_colorbar=True, add_labels=True, vmin=None, vmax=None,
cmap=None, center=None, robust=False, extend=None,
levels=None, colors=None, subplot_kws=None, **kwargs):
# All 2d plots in xarray share this function signature.
# Method signature below should be consistent.
# Handle facetgrids first
if row or col:
allargs = locals().copy()
allargs.update(allargs.pop('kwargs'))
# Need the decorated plotting function
allargs['plotfunc'] = globals()[plotfunc.__name__]
return _easy_facetgrid(**allargs)
import matplotlib.pyplot as plt
# 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)
if ax is None:
ax = plt.gca()
xlab, ylab = _infer_xy_labels(darray=darray, x=x, y=y)
# better to pass the ndarrays directly to plotting functions
xval = darray[xlab].values
yval = darray[ylab].values
zval = darray.to_masked_array(copy=False)
# May need to transpose for correct x, y labels
if xlab == darray.dims[0]:
zval = zval.T
_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']
# This allows the user to pass in a custom norm coming via kwargs
kwargs.setdefault('norm', cmap_params['norm'])
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 = plt.colorbar(primitive, ax=ax, extend=cmap_params['extend'])
if darray.name and add_labels:
cbar.set_label(darray.name, rotation=90)
_update_axes_limits(ax, xincrease, yincrease)
return primitive
# For use as DataArray.plot.plotmethod
@functools.wraps(newplotfunc)
def plotmethod(_PlotMethods_obj, x=None, y=None, ax=None, row=None,
col=None, col_wrap=None, xincrease=True, yincrease=True,
add_colorbar=True, add_labels=True, vmin=None, vmax=None,
cmap=None, colors=None, center=None, robust=False,
extend=None, levels=None, subplot_kws=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 matplotlib.pyplot.imshow
..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
xstep = (x[1] - x[0]) / 2.0
ystep = (y[1] - y[0]) / 2.0
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)
primitive = ax.imshow(z, **defaults)
return ax, primitive
@_plot2d
def contour(x, y, z, ax, **kwargs):
"""
Contour plot of 2d DataArray
Wraps matplotlib.pyplot.contour
"""
primitive = ax.contour(x, y, z, **kwargs)
return ax, primitive
@_plot2d
def contourf(x, y, z, ax, **kwargs):
"""
Filled contour plot of 2d DataArray
Wraps matplotlib.pyplot.contourf
"""
primitive = ax.contourf(x, y, z, **kwargs)
return ax, primitive
def _infer_interval_breaks(coord):
"""
>>> _infer_interval_breaks(np.arange(5))
array([-0.5, 0.5, 1.5, 2.5, 3.5, 4.5])
"""
coord = np.asarray(coord)
deltas = 0.5 * (coord[1:] - coord[:-1])
first = coord[0] - deltas[0]
last = coord[-1] + deltas[-1]
return np.r_[[first], coord[:-1] + deltas, [last]]
@_plot2d
def pcolormesh(x, y, z, ax, **kwargs):
"""
Pseudocolor plot of 2d DataArray
Wraps matplotlib.pyplot.pcolormesh
"""
x = _infer_interval_breaks(x)
y = _infer_interval_breaks(y)
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 ax, primitive
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