""" 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.common import contains_cftime_datetimes from xarray.core.pycompat import basestring from .facetgrid import FacetGrid from .utils import ( ROBUST_PERCENTILE, _determine_cmap_params, _infer_xy_labels, _interval_to_double_bound_points, _interval_to_mid_points, _resolve_intervals_2dplot, _valid_other_type, get_axis, import_matplotlib_pyplot, label_from_attrs) 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 _ensure_plottable(*args): """ Raise exception if there is anything in args that can't be plotted on an axis by matplotlib. """ 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 of type np.datetime64 or ' 'datetime.datetime or pd.Interval.') 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 _line_facetgrid(darray, row=None, col=None, hue=None, col_wrap=None, sharex=True, sharey=True, aspect=None, size=None, subplot_kws=None, **kwargs): """ Convenience method to call xarray.plot.FacetGrid for line plots kwargs are the arguments to pyplot.plot() """ 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_line(hue=hue, **kwargs) def plot(darray, row=None, col=None, col_wrap=None, ax=None, hue=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 hue : string, optional If passed, make faceted line plots with hue 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() if contains_cftime_datetimes(darray): raise NotImplementedError( 'Built-in plotting of arrays of cftime.datetime objects or arrays ' 'indexed by cftime.datetime objects is currently not implemented ' 'within xarray. A possible workaround is to use the ' 'nc-time-axis package ' '(https://github.com/SciTools/nc-time-axis) to convert the dates ' 'to a plottable type and plot your data directly with matplotlib.') plot_dims = set(darray.dims) plot_dims.discard(row) plot_dims.discard(col) plot_dims.discard(hue) ndims = len(plot_dims) error_msg = ('Only 1d and 2d plots are supported for facets in xarray. ' 'See the package `Seaborn` for more options.') if ndims in [1, 2]: if row or col: kwargs['row'] = row kwargs['col'] = col kwargs['col_wrap'] = col_wrap kwargs['subplot_kws'] = subplot_kws if ndims == 1: plotfunc = line kwargs['hue'] = hue elif ndims == 2: if hue: plotfunc = line kwargs['hue'] = hue else: plotfunc = pcolormesh else: if row or col or hue: raise ValueError(error_msg) plotfunc = hist kwargs['ax'] = ax return plotfunc(darray, **kwargs) def _infer_line_data(darray, x, y, hue): error_msg = ('must be either None or one of ({0:s})' .format(', '.join([repr(dd) for dd in darray.dims]))) ndims = len(darray.dims) if x is not None and x not in darray.dims and x not in darray.coords: raise ValueError('x ' + error_msg) if y is not None and y not in darray.dims and y not in darray.coords: 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 huename = None hueplt = None huelabel = '' if (x is None and y is None) or x == dim: xplt = darray[dim] yplt = darray else: yplt = darray[dim] xplt = darray else: if x is None and y is None and hue is None: raise ValueError('For 2D inputs, please' 'specify either hue, x or y.') if y is None: xname, huename = _infer_xy_labels(darray=darray, x=x, y=hue) xplt = darray[xname] if xplt.ndim > 1: if huename in darray.dims: otherindex = 1 if darray.dims.index(huename) == 0 else 0 otherdim = darray.dims[otherindex] yplt = darray.transpose(otherdim, huename) xplt = xplt.transpose(otherdim, huename) else: raise ValueError('For 2D inputs, hue must be a dimension' + ' i.e. one of ' + repr(darray.dims)) else: yplt = darray.transpose(xname, huename) else: yname, huename = _infer_xy_labels(darray=darray, x=y, y=hue) yplt = darray[yname] if yplt.ndim > 1: if huename in darray.dims: otherindex = 1 if darray.dims.index(huename) == 0 else 0 xplt = darray.transpose(otherdim, huename) else: raise ValueError('For 2D inputs, hue must be a dimension' + ' i.e. one of ' + repr(darray.dims)) else: xplt = darray.transpose(yname, huename) huelabel = label_from_attrs(darray[huename]) hueplt = darray[huename] xlabel = label_from_attrs(xplt) ylabel = label_from_attrs(yplt) return xplt, yplt, hueplt, xlabel, ylabel, huelabel # 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 Dimension or coordinate for which you want multiple lines plotted. If plotting against a 2D coordinate, ``hue`` must be a dimension. x, y : string, optional Dimensions or 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. xscale, yscale : 'linear', 'symlog', 'log', 'logit', optional Specifies scaling for the x- and y-axes respectively xticks, yticks : Specify tick locations for x- and y-axes xlim, ylim : Specify x- and y-axes limits 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_legend : boolean, optional Add legend with y axis coordinates (2D inputs only). *args, **kwargs : optional Additional arguments to matplotlib.pyplot.plot """ # Handle facetgrids first row = kwargs.pop('row', None) col = kwargs.pop('col', None) if row or col: allargs = locals().copy() allargs.update(allargs.pop('kwargs')) return _line_facetgrid(**allargs) 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) xincrease = kwargs.pop('xincrease', None) # default needs to be None yincrease = kwargs.pop('yincrease', None) xscale = kwargs.pop('xscale', None) # default needs to be None yscale = kwargs.pop('yscale', None) xticks = kwargs.pop('xticks', None) yticks = kwargs.pop('yticks', None) xlim = kwargs.pop('xlim', None) ylim = kwargs.pop('ylim', None) add_legend = kwargs.pop('add_legend', True) _labels = kwargs.pop('_labels', True) if args is (): args = kwargs.pop('args', ()) ax = get_axis(figsize, size, aspect, ax) xplt, yplt, hueplt, xlabel, ylabel, huelabel = \ _infer_line_data(darray, x, y, hue) # Remove pd.Intervals if contained in xplt.values. if _valid_other_type(xplt.values, [pd.Interval]): # Is it a step plot? (see matplotlib.Axes.step) if kwargs.get('linestyle', '').startswith('steps-'): xplt_val, yplt_val = _interval_to_double_bound_points(xplt.values, yplt.values) # Remove steps-* to be sure that matplotlib is not confused kwargs['linestyle'] = (kwargs['linestyle'] .replace('steps-pre', '') .replace('steps-post', '') .replace('steps-mid', '')) if kwargs['linestyle'] == '': kwargs.pop('linestyle') else: xplt_val = _interval_to_mid_points(xplt.values) yplt_val = yplt.values xlabel += '_center' else: xplt_val = xplt.values yplt_val = yplt.values _ensure_plottable(xplt_val, yplt_val) primitive = ax.plot(xplt_val, yplt_val, *args, **kwargs) if _labels: 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(hueplt.values), title=huelabel) # Rotate dates on xlabels # Do this without calling autofmt_xdate so that x-axes ticks # on other subplots (if any) are not deleted. # https://stackoverflow.com/questions/17430105/autofmt-xdate-deletes-x-axis-labels-of-all-subplots if np.issubdtype(xplt.dtype, np.datetime64): for xlabels in ax.get_xticklabels(): xlabels.set_rotation(30) xlabels.set_ha('right') _update_axes(ax, xincrease, yincrease, xscale, yscale, xticks, yticks, xlim, ylim) return primitive def step(darray, *args, **kwargs): """ Step plot of DataArray index against values Similar to :func:`matplotlib:matplotlib.pyplot.step` Parameters ---------- where : {'pre', 'post', 'mid'}, optional, default 'pre' Define where the steps should be placed: - 'pre': The y value is continued constantly to the left from every *x* position, i.e. the interval ``(x[i-1], x[i]]`` has the value ``y[i]``. - 'post': The y value is continued constantly to the right from every *x* position, i.e. the interval ``[x[i], x[i+1])`` has the value ``y[i]``. - 'mid': Steps occur half-way between the *x* positions. Note that this parameter is ignored if the x coordinate consists of :py:func:`pandas.Interval` values, e.g. as a result of :py:func:`xarray.Dataset.groupby_bins`. In this case, the actual boundaries of the interval are used. *args, **kwargs : optional Additional arguments following :py:func:`xarray.plot.line` """ if ('ls' in kwargs.keys()) and ('linestyle' not in kwargs.keys()): kwargs['linestyle'] = kwargs.pop('ls') where = kwargs.pop('where', 'pre') if where not in ('pre', 'post', 'mid'): raise ValueError("'where' argument to step must be " "'pre', 'post' or 'mid'") kwargs['linestyle'] = 'steps-' + where + kwargs.get('linestyle', '') return line(darray, *args, **kwargs) 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) xincrease = kwargs.pop('xincrease', None) # default needs to be None yincrease = kwargs.pop('yincrease', None) xscale = kwargs.pop('xscale', None) # default needs to be None yscale = kwargs.pop('yscale', None) xticks = kwargs.pop('xticks', None) yticks = kwargs.pop('yticks', None) xlim = kwargs.pop('xlim', None) ylim = kwargs.pop('ylim', None) no_nan = np.ravel(darray.values) no_nan = no_nan[pd.notnull(no_nan)] primitive = ax.hist(no_nan, **kwargs) ax.set_title('Histogram') ax.set_xlabel(label_from_attrs(darray)) _update_axes(ax, xincrease, yincrease, xscale, yscale, xticks, yticks, xlim, ylim) return primitive def _update_axes(ax, xincrease, yincrease, xscale=None, yscale=None, xticks=None, yticks=None, xlim=None, ylim=None): """ Update axes with provided parameters """ if xincrease is None: pass elif xincrease and ax.xaxis_inverted(): ax.invert_xaxis() elif not xincrease and not ax.xaxis_inverted(): ax.invert_xaxis() if yincrease is None: pass elif yincrease and ax.yaxis_inverted(): ax.invert_yaxis() elif not yincrease and not ax.yaxis_inverted(): ax.invert_yaxis() # The default xscale, yscale needs to be None. # If we set a scale it resets the axes formatters, # This means that set_xscale('linear') on a datetime axis # will remove the date labels. So only set the scale when explicitly # asked to. https://github.com/matplotlib/matplotlib/issues/8740 if xscale is not None: ax.set_xscale(xscale) if yscale is not None: ax.set_yscale(yscale) if xticks is not None: ax.set_xticks(xticks) if yticks is not None: ax.set_yticks(yticks) if xlim is not None: ax.set_xlim(xlim) if ylim is not None: ax.set_ylim(ylim) # 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) @functools.wraps(step) def step(self, *args, **kwargs): return step(self._da, *args, **kwargs) def _rescale_imshow_rgb(darray, vmin, vmax, robust): assert robust or vmin is not None or vmax is not None # TODO: remove when min numpy version is bumped to 1.13 # 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') with warnings.catch_warnings(): warnings.filterwarnings('ignore', 'xarray.ufuncs', PendingDeprecationWarning) 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 xscale, yscale : 'linear', 'symlog', 'log', 'logit', optional Specifies scaling for the x- and y-axes respectively xticks, yticks : Specify tick locations for x- and y-axes xlim, ylim : Specify x- and y-axes limits 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 norm : ``matplotlib.colors.Normalize`` instance, optional If the ``norm`` has vmin or vmax specified, the corresponding kwarg must be None. 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, xscale=None, yscale=None, xticks=None, yticks=None, xlim=None, ylim=None, norm=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) # Replace pd.Intervals if contained in xval or yval. xplt, xlab_extra = _resolve_intervals_2dplot(xval, plotfunc.__name__) yplt, ylab_extra = _resolve_intervals_2dplot(yval, plotfunc.__name__) _ensure_plottable(xplt, yplt) 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', 'norm': norm, } 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 colors == a single color, matplotlib draws dashed negative # contours. we lose this feature if we pass cmap and not colors if isinstance(colors, basestring): cmap_params['cmap'] = None kwargs['colors'] = colors if 'pcolormesh' == plotfunc.__name__: kwargs['infer_intervals'] = infer_intervals 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(xplt, yplt, zval, ax=ax, cmap=cmap_params['cmap'], vmin=cmap_params['vmin'], vmax=cmap_params['vmax'], norm=cmap_params['norm'], **kwargs) # Label the plot with metadata if add_labels: ax.set_xlabel(label_from_attrs(darray[xlab], xlab_extra)) ax.set_ylabel(label_from_attrs(darray[ylab], ylab_extra)) 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 add_labels and 'label' not in cbar_kwargs: cbar.set_label(label_from_attrs(darray)) 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.") # origin kwarg overrides yincrease if 'origin' in kwargs: yincrease = None _update_axes(ax, xincrease, yincrease, xscale, yscale, xticks, yticks, xlim, ylim) # Rotate dates on xlabels # Do this without calling autofmt_xdate so that x-axes ticks # on other subplots (if any) are not deleted. # https://stackoverflow.com/questions/17430105/autofmt-xdate-deletes-x-axis-labels-of-all-subplots if np.issubdtype(xplt.dtype, np.datetime64): for xlabels in ax.get_xticklabels(): xlabels.set_rotation(30) xlabels.set_ha('right') 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, xscale=None, yscale=None, xticks=None, yticks=None, xlim=None, ylim=None, norm=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 `. .. 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 = {'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 defaults['origin'] == 'upper': defaults['extent'] = [left, right, bottom, top] else: defaults['extent'] = [left, right, top, bottom] 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, check_monotonic=False): """ >>> _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 check_monotonic and 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 and ((np.shape(x)[0] == np.shape(z)[1]) or ((x.ndim > 1) and (np.shape(x)[1] == np.shape(z)[1])))): if len(x.shape) == 1: x = _infer_interval_breaks(x, check_monotonic=True) else: # we have to infer the intervals on both axes x = _infer_interval_breaks(x, axis=1) x = _infer_interval_breaks(x, axis=0) if (infer_intervals and (np.shape(y)[0] == np.shape(z)[0])): if len(y.shape) == 1: y = _infer_interval_breaks(y, check_monotonic=True) else: # we have to infer the intervals on both axes 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