""" Use this module directly: import xarray.plot as xplt Or use the methods on a DataArray or Dataset: DataArray.plot._____ Dataset.plot._____ """ import functools import numpy as np import pandas as pd from .facetgrid import _easy_facetgrid from .utils import ( _add_colorbar, _assert_valid_xy, _ensure_plottable, _infer_interval_breaks, _infer_xy_labels, _process_cmap_cbar_kwargs, _rescale_imshow_rgb, _resolve_intervals_1dplot, _resolve_intervals_2dplot, _update_axes, get_axis, import_matplotlib_pyplot, label_from_attrs, ) def _infer_line_data(darray, x, y, hue): ndims = len(darray.dims) if x is not None and y is not None: raise ValueError("Cannot specify both x and y kwargs for line plots.") if x is not None: _assert_valid_xy(darray, x, "x") if y is not None: _assert_valid_xy(darray, y, "y") if ndims == 1: huename = None hueplt = None huelabel = "" if x is not None: xplt = darray[x] yplt = darray elif y is not None: xplt = darray yplt = darray[y] else: # Both x & y are None dim = darray.dims[0] xplt = darray[dim] yplt = 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, transpose_coords=False) xplt = xplt.transpose(otherdim, huename, transpose_coords=False) else: raise ValueError( "For 2D inputs, hue must be a dimension" " i.e. one of " + repr(darray.dims) ) else: (xdim,) = darray[xname].dims (huedim,) = darray[huename].dims yplt = darray.transpose(xdim, huedim) 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 otherdim = darray.dims[otherindex] xplt = darray.transpose(otherdim, huename, transpose_coords=False) yplt = yplt.transpose(otherdim, huename, transpose_coords=False) else: raise ValueError( "For 2D inputs, hue must be a dimension" " i.e. one of " + repr(darray.dims) ) else: (ydim,) = darray[yname].dims (huedim,) = darray[huename].dims xplt = darray.transpose(ydim, huedim) 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 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 : str, optional If passed, make row faceted plots on this dimension name col : str, optional If passed, make column faceted plots on this dimension name hue : str, optional If passed, make faceted line plots with hue on this dimension name col_wrap : int, optional Use together with ``col`` to wrap faceted plots ax : matplotlib.axes.Axes, optional If None, uses the current axis. Not applicable when using facets. rtol : float, 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. **kwargs : optional Additional keyword arguments to matplotlib """ darray = darray.squeeze().compute() 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["subplot_kws"] = subplot_kws kwargs["row"] = row kwargs["col"] = col kwargs["col_wrap"] = col_wrap if ndims == 1: plotfunc = line kwargs["hue"] = hue elif ndims == 2: if hue: plotfunc = line kwargs["hue"] = hue else: plotfunc = pcolormesh kwargs["subplot_kws"] = subplot_kws else: if row or col or hue: 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, row=None, col=None, figsize=None, aspect=None, size=None, ax=None, hue=None, x=None, y=None, xincrease=None, yincrease=None, xscale=None, yscale=None, xticks=None, yticks=None, xlim=None, ylim=None, add_legend=True, _labels=True, **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 Dimension, coordinate or MultiIndex level 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 : bool, optional Add legend with y axis coordinates (2D inputs only). *args, **kwargs : optional Additional arguments to matplotlib.pyplot.plot """ # Handle facetgrids first if row or col: allargs = locals().copy() allargs.update(allargs.pop("kwargs")) allargs.pop("darray") return _easy_facetgrid(darray, line, kind="line", **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) ) # The allargs dict passed to _easy_facetgrid above contains args if args == (): args = kwargs.pop("args", ()) else: assert "args" not in kwargs ax = get_axis(figsize, size, aspect, ax) xplt, yplt, hueplt, xlabel, ylabel, hue_label = _infer_line_data(darray, x, y, hue) # Remove pd.Intervals if contained in xplt.values and/or yplt.values. xplt_val, yplt_val, xlabel, ylabel, kwargs = _resolve_intervals_1dplot( xplt.values, yplt.values, xlabel, ylabel, kwargs ) _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=hue_label) # 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, where="pre", drawstyle=None, ds=None, **kwargs): """ Step plot of DataArray index against values Similar to :func:`matplotlib:matplotlib.pyplot.step` Parameters ---------- where : {"pre", "post", "mid"}, 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 one 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 where not in {"pre", "post", "mid"}: raise ValueError("'where' argument to step must be 'pre', 'post' or 'mid'") if ds is not None: if drawstyle is None: drawstyle = ds else: raise TypeError("ds and drawstyle are mutually exclusive") if drawstyle is None: drawstyle = "" drawstyle = "steps-" + where + drawstyle return line(darray, *args, drawstyle=drawstyle, **kwargs) def hist( darray, figsize=None, size=None, aspect=None, ax=None, xincrease=None, yincrease=None, xscale=None, yscale=None, xticks=None, yticks=None, xlim=None, ylim=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.Axes, 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_title("Histogram") ax.set_xlabel(label_from_attrs(darray)) _update_axes(ax, xincrease, yincrease, xscale, yscale, xticks, yticks, xlim, ylim) return primitive # MUST run before any 2d plotting functions are defined since # _plot2d decorator adds them as methods here. class _PlotMethods: """ Enables use of xarray.plot functions as attributes on a DataArray. For example, DataArray.plot.imshow """ __slots__ = ("_da",) def __init__(self, darray): self._da = darray def __call__(self, **kwargs): return plot(self._da, **kwargs) # we can't use functools.wraps here since that also modifies the name / qualname __doc__ = __call__.__doc__ = plot.__doc__ __call__.__wrapped__ = plot # type: ignore __call__.__annotations__ = plot.__annotations__ @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 override_signature(f): def wrapper(func): func.__wrapped__ = f return func return wrapper 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 : int, 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 : bool, optional Adds colorbar to axis add_labels : bool, 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 used for 2D and FacetGrid plots. 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__ = f"{plotfunc.__doc__}\n{commondoc}" # plotfunc and newplotfunc have different signatures: # - plotfunc: (x, y, z, ax, **kwargs) # - newplotfunc: (darray, x, y, **kwargs) # where plotfunc accepts numpy arrays, while newplotfunc accepts a DataArray # and variable names. newplotfunc also explicitly lists most kwargs, so we # need to shorten it def signature(darray, x, y, **kwargs): pass @override_signature(signature) @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() del allargs["darray"] del allargs["imshow_rgb"] allargs.update(allargs.pop("kwargs")) # Need the decorated plotting function allargs["plotfunc"] = globals()[plotfunc.__name__] return _easy_facetgrid(darray, kind="dataarray", **allargs) plt = import_matplotlib_pyplot() rgb = kwargs.pop("rgb", None) 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)" ) xlab, ylab = _infer_xy_labels( darray=darray, x=x, y=y, imshow=imshow_rgb, rgb=rgb ) # 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: dims = darray[ylab].dims if xval.shape[0] == yval.shape[0]: xval = np.broadcast_to(xval[:, np.newaxis], yval.shape) else: xval = np.broadcast_to(xval[np.newaxis, :], yval.shape) elif yval.ndim < xval.ndim: dims = darray[xlab].dims if yval.shape[0] == xval.shape[0]: yval = np.broadcast_to(yval[:, np.newaxis], xval.shape) else: yval = np.broadcast_to(yval[np.newaxis, :], xval.shape) elif xval.ndim == 2: dims = darray[xlab].dims else: dims = (darray[ylab].dims[0], darray[xlab].dims[0]) # 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, transpose_coords=True) # 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, zval) cmap_params, cbar_kwargs = _process_cmap_cbar_kwargs( plotfunc, zval.data, **locals(), _is_facetgrid=kwargs.pop("_is_facetgrid", False), ) 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, str): cmap_params["cmap"] = None kwargs["colors"] = colors if "pcolormesh" == plotfunc.__name__: kwargs["infer_intervals"] = infer_intervals if "imshow" == plotfunc.__name__ and isinstance(aspect, str): # forbid usage of mpl strings raise ValueError("plt.imshow's `aspect` kwarg is not available in xarray") if subplot_kws is None: subplot_kws = dict() ax = get_axis(figsize, size, aspect, ax, **subplot_kws) 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: if add_labels and "label" not in cbar_kwargs: cbar_kwargs["label"] = label_from_attrs(darray) cbar = _add_colorbar(primitive, ax, cbar_ax, cbar_kwargs, cmap_params) elif cbar_ax is not None or cbar_kwargs: # 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 = 0.1 try: ystep = (y[1] - y[0]) / 2.0 except IndexError: ystep = 0.1 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 @_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