"""

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

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

if darray[xlab].dims[-1] == 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

"""

if not hasattr(ax, 'projection'):

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