from __future__ import annotations import functools import inspect import warnings from typing import TYPE_CHECKING, Any, Callable, Hashable, Iterable, TypeVar, overload from xarray.core.alignment import broadcast from xarray.plot import dataarray_plot from xarray.plot.facetgrid import _easy_facetgrid from xarray.plot.utils import ( _add_colorbar, _get_nice_quiver_magnitude, _infer_meta_data, _process_cmap_cbar_kwargs, get_axis, ) if TYPE_CHECKING: from matplotlib.axes import Axes from matplotlib.collections import LineCollection, PathCollection from matplotlib.colors import Colormap, Normalize from matplotlib.quiver import Quiver from numpy.typing import ArrayLike from xarray.core.dataarray import DataArray from xarray.core.dataset import Dataset from xarray.core.types import ( AspectOptions, ExtendOptions, HueStyleOptions, ScaleOptions, ) from xarray.plot.facetgrid import FacetGrid def _dsplot(plotfunc): commondoc = """ Parameters ---------- ds : Dataset x : Hashable or None, optional Variable name for x-axis. y : Hashable or None, optional Variable name for y-axis. u : Hashable or None, optional Variable name for the *u* velocity (in *x* direction). quiver/streamplot plots only. v : Hashable or None, optional Variable name for the *v* velocity (in *y* direction). quiver/streamplot plots only. hue: Hashable or None, optional Variable by which to color scatter points or arrows. hue_style: {'continuous', 'discrete'} or None, optional How to use the ``hue`` variable: - ``'continuous'`` -- continuous color scale (default for numeric ``hue`` variables) - ``'discrete'`` -- a color for each unique value, using the default color cycle (default for non-numeric ``hue`` variables) row : Hashable or None, optional If passed, make row faceted plots on this dimension name. col : Hashable or None, optional If passed, make column faceted plots on this dimension name. col_wrap : int, optional Use together with ``col`` to wrap faceted plots. ax : matplotlib axes object or None, optional If ``None``, use the current axes. Not applicable when using facets. figsize : Iterable[float] or None, optional A tuple (width, height) of the figure in inches. Mutually exclusive with ``size`` and ``ax``. 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``. aspect : "auto", "equal", scalar or None, optional Aspect ratio of plot, so that ``aspect * size`` gives the width in inches. Only used if a ``size`` is provided. sharex : bool or None, optional If True all subplots share the same x-axis. sharey : bool or None, optional If True all subplots share the same y-axis. add_guide: bool or None, optional Add a guide that depends on ``hue_style``: - ``'continuous'`` -- build a colorbar - ``'discrete'`` -- build a legend subplot_kws : dict or None, optional Dictionary of keyword arguments for Matplotlib subplots (see :py:meth:`matplotlib:matplotlib.figure.Figure.add_subplot`). Only applies to FacetGrid plotting. cbar_kwargs : dict, optional Dictionary of keyword arguments to pass to the colorbar (see :meth:`matplotlib:matplotlib.figure.Figure.colorbar`). cbar_ax : matplotlib axes object, optional Axes in which to draw the colorbar. cmap : matplotlib colormap name or colormap, optional The mapping from data values to color space. Either a Matplotlib colormap name or object. If not provided, this will be either ``'viridis'`` (if the function infers a sequential dataset) or ``'RdBu_r'`` (if the function infers a diverging dataset). See :doc:`Choosing Colormaps in Matplotlib ` for more information. If *seaborn* is installed, ``cmap`` may also be a `seaborn color palette `_. Note: if ``cmap`` is a seaborn color palette, ``levels`` must also be specified. vmin : float or None, optional Lower value to anchor the colormap, otherwise it is inferred from the data and other keyword arguments. When a diverging dataset is inferred, setting `vmin` or `vmax` 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. vmax : float or None, optional Upper value to anchor the colormap, otherwise it is inferred from the data and other keyword arguments. When a diverging dataset is inferred, setting `vmin` or `vmax` 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. norm : matplotlib.colors.Normalize, optional If ``norm`` has ``vmin`` or ``vmax`` specified, the corresponding kwarg must be ``None``. infer_intervals: bool | None If True the intervals are infered. 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. colors : str or array-like of color-like, optional A single color or a list of colors. The ``levels`` argument is required. 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 array-like, 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)``. **kwargs : optional Additional keyword arguments to wrapped Matplotlib function. """ # Build on the original docstring plotfunc.__doc__ = f"{plotfunc.__doc__}\n{commondoc}" @functools.wraps( plotfunc, assigned=("__module__", "__name__", "__qualname__", "__doc__") ) def newplotfunc( ds: Dataset, *args: Any, x: Hashable | None = None, y: Hashable | None = None, u: Hashable | None = None, v: Hashable | None = None, hue: Hashable | None = None, hue_style: HueStyleOptions = None, row: Hashable | None = None, col: Hashable | None = None, col_wrap: int | None = None, ax: Axes | None = None, figsize: Iterable[float] | None = None, size: float | None = None, aspect: AspectOptions = None, sharex: bool = True, sharey: bool = True, add_guide: bool | None = None, subplot_kws: dict[str, Any] | None = None, cbar_kwargs: dict[str, Any] | None = None, cbar_ax: Axes | None = None, cmap: str | Colormap | None = None, vmin: float | None = None, vmax: float | None = None, norm: Normalize | None = None, infer_intervals: bool | None = None, center: float | None = None, robust: bool | None = None, colors: str | ArrayLike | None = None, extend: ExtendOptions = None, levels: ArrayLike | None = None, **kwargs: Any, ) -> Any: if args: # TODO: Deprecated since 2022.10: msg = "Using positional arguments is deprecated for plot methods, use keyword arguments instead." assert x is None x = args[0] if len(args) > 1: assert y is None y = args[1] if len(args) > 2: assert u is None u = args[2] if len(args) > 3: assert v is None v = args[3] if len(args) > 4: assert hue is None hue = args[4] if len(args) > 5: raise ValueError(msg) else: warnings.warn(msg, DeprecationWarning, stacklevel=2) del args _is_facetgrid = kwargs.pop("_is_facetgrid", False) if _is_facetgrid: # facetgrid call meta_data = kwargs.pop("meta_data") else: meta_data = _infer_meta_data( ds, x, y, hue, hue_style, add_guide, funcname=plotfunc.__name__ ) hue_style = meta_data["hue_style"] # handle facetgrids first if col or row: allargs = locals().copy() allargs["plotfunc"] = globals()[plotfunc.__name__] allargs["data"] = ds # remove kwargs to avoid passing the information twice for arg in ["meta_data", "kwargs", "ds"]: del allargs[arg] return _easy_facetgrid(kind="dataset", **allargs, **kwargs) figsize = kwargs.pop("figsize", None) ax = get_axis(figsize, size, aspect, ax) if hue_style == "continuous" and hue is not None: if _is_facetgrid: cbar_kwargs = meta_data["cbar_kwargs"] cmap_params = meta_data["cmap_params"] else: cmap_params, cbar_kwargs = _process_cmap_cbar_kwargs( plotfunc, ds[hue].values, **locals() ) # subset that can be passed to scatter, hist2d cmap_params_subset = { vv: cmap_params[vv] for vv in ["vmin", "vmax", "norm", "cmap"] } else: cmap_params_subset = {} if (u is not None or v is not None) and plotfunc.__name__ not in ( "quiver", "streamplot", ): raise ValueError("u, v are only allowed for quiver or streamplot plots.") primitive = plotfunc( ds=ds, x=x, y=y, ax=ax, u=u, v=v, hue=hue, hue_style=hue_style, cmap_params=cmap_params_subset, **kwargs, ) if _is_facetgrid: # if this was called from Facetgrid.map_dataset, return primitive # finish here. Else, make labels if meta_data.get("xlabel", None): ax.set_xlabel(meta_data.get("xlabel")) if meta_data.get("ylabel", None): ax.set_ylabel(meta_data.get("ylabel")) if meta_data["add_legend"]: ax.legend(handles=primitive, title=meta_data.get("hue_label", None)) if meta_data["add_colorbar"]: cbar_kwargs = {} if cbar_kwargs is None else cbar_kwargs if "label" not in cbar_kwargs: cbar_kwargs["label"] = meta_data.get("hue_label", None) _add_colorbar(primitive, ax, cbar_ax, cbar_kwargs, cmap_params) if meta_data["add_quiverkey"]: magnitude = _get_nice_quiver_magnitude(ds[u], ds[v]) units = ds[u].attrs.get("units", "") ax.quiverkey( primitive, X=0.85, Y=0.9, U=magnitude, label=f"{magnitude}\n{units}", labelpos="E", coordinates="figure", ) if plotfunc.__name__ in ("quiver", "streamplot"): title = ds[u]._title_for_slice() else: title = ds[x]._title_for_slice() ax.set_title(title) return primitive # we want to actually expose the signature of newplotfunc # and not the copied **kwargs from the plotfunc which # functools.wraps adds, so delete the wrapped attr del newplotfunc.__wrapped__ return newplotfunc @overload def quiver( ds: Dataset, *args: Any, x: Hashable | None = None, y: Hashable | None = None, u: Hashable | None = None, v: Hashable | None = None, hue: Hashable | None = None, hue_style: HueStyleOptions = None, col: None = None, # no wrap -> primitive row: None = None, # no wrap -> primitive ax: Axes | None = None, figsize: Iterable[float] | None = None, size: float | None = None, col_wrap: int | None = None, sharex: bool = True, sharey: bool = True, aspect: AspectOptions = None, subplot_kws: dict[str, Any] | None = None, add_guide: bool | None = None, cbar_kwargs: dict[str, Any] | None = None, cbar_ax: Axes | None = None, vmin: float | None = None, vmax: float | None = None, norm: Normalize | None = None, infer_intervals: bool | None = None, center: float | None = None, levels: ArrayLike | None = None, robust: bool | None = None, colors: str | ArrayLike | None = None, extend: ExtendOptions = None, cmap: str | Colormap | None = None, **kwargs: Any, ) -> Quiver: ... @overload def quiver( ds: Dataset, *args: Any, x: Hashable | None = None, y: Hashable | None = None, u: Hashable | None = None, v: Hashable | None = None, hue: Hashable | None = None, hue_style: HueStyleOptions = None, col: Hashable, # wrap -> FacetGrid row: Hashable | None = None, ax: Axes | None = None, figsize: Iterable[float] | None = None, size: float | None = None, col_wrap: int | None = None, sharex: bool = True, sharey: bool = True, aspect: AspectOptions = None, subplot_kws: dict[str, Any] | None = None, add_guide: bool | None = None, cbar_kwargs: dict[str, Any] | None = None, cbar_ax: Axes | None = None, vmin: float | None = None, vmax: float | None = None, norm: Normalize | None = None, infer_intervals: bool | None = None, center: float | None = None, levels: ArrayLike | None = None, robust: bool | None = None, colors: str | ArrayLike | None = None, extend: ExtendOptions = None, cmap: str | Colormap | None = None, **kwargs: Any, ) -> FacetGrid[Dataset]: ... @overload def quiver( ds: Dataset, *args: Any, x: Hashable | None = None, y: Hashable | None = None, u: Hashable | None = None, v: Hashable | None = None, hue: Hashable | None = None, hue_style: HueStyleOptions = None, col: Hashable | None = None, row: Hashable, # wrap -> FacetGrid ax: Axes | None = None, figsize: Iterable[float] | None = None, size: float | None = None, col_wrap: int | None = None, sharex: bool = True, sharey: bool = True, aspect: AspectOptions = None, subplot_kws: dict[str, Any] | None = None, add_guide: bool | None = None, cbar_kwargs: dict[str, Any] | None = None, cbar_ax: Axes | None = None, vmin: float | None = None, vmax: float | None = None, norm: Normalize | None = None, infer_intervals: bool | None = None, center: float | None = None, levels: ArrayLike | None = None, robust: bool | None = None, colors: str | ArrayLike | None = None, extend: ExtendOptions = None, cmap: str | Colormap | None = None, **kwargs: Any, ) -> FacetGrid[Dataset]: ... @_dsplot def quiver( ds: Dataset, x: Hashable, y: Hashable, ax: Axes, u: Hashable, v: Hashable, **kwargs: Any, ) -> Quiver: """Quiver plot of Dataset variables. Wraps :py:func:`matplotlib:matplotlib.pyplot.quiver`. """ import matplotlib as mpl if x is None or y is None or u is None or v is None: raise ValueError("Must specify x, y, u, v for quiver plots.") dx, dy, du, dv = broadcast(ds[x], ds[y], ds[u], ds[v]) args = [dx.values, dy.values, du.values, dv.values] hue = kwargs.pop("hue") cmap_params = kwargs.pop("cmap_params") if hue: args.append(ds[hue].values) # TODO: Fix this by always returning a norm with vmin, vmax in cmap_params if not cmap_params["norm"]: cmap_params["norm"] = mpl.colors.Normalize( cmap_params.pop("vmin"), cmap_params.pop("vmax") ) kwargs.pop("hue_style") kwargs.setdefault("pivot", "middle") hdl = ax.quiver(*args, **kwargs, **cmap_params) return hdl @overload def streamplot( ds: Dataset, *args: Any, x: Hashable | None = None, y: Hashable | None = None, u: Hashable | None = None, v: Hashable | None = None, hue: Hashable | None = None, hue_style: HueStyleOptions = None, col: None = None, # no wrap -> primitive row: None = None, # no wrap -> primitive ax: Axes | None = None, figsize: Iterable[float] | None = None, size: float | None = None, col_wrap: int | None = None, sharex: bool = True, sharey: bool = True, aspect: AspectOptions = None, subplot_kws: dict[str, Any] | None = None, add_guide: bool | None = None, cbar_kwargs: dict[str, Any] | None = None, cbar_ax: Axes | None = None, vmin: float | None = None, vmax: float | None = None, norm: Normalize | None = None, infer_intervals: bool | None = None, center: float | None = None, levels: ArrayLike | None = None, robust: bool | None = None, colors: str | ArrayLike | None = None, extend: ExtendOptions = None, cmap: str | Colormap | None = None, **kwargs: Any, ) -> LineCollection: ... @overload def streamplot( ds: Dataset, *args: Any, x: Hashable | None = None, y: Hashable | None = None, u: Hashable | None = None, v: Hashable | None = None, hue: Hashable | None = None, hue_style: HueStyleOptions = None, col: Hashable, # wrap -> FacetGrid row: Hashable | None = None, ax: Axes | None = None, figsize: Iterable[float] | None = None, size: float | None = None, col_wrap: int | None = None, sharex: bool = True, sharey: bool = True, aspect: AspectOptions = None, subplot_kws: dict[str, Any] | None = None, add_guide: bool | None = None, cbar_kwargs: dict[str, Any] | None = None, cbar_ax: Axes | None = None, vmin: float | None = None, vmax: float | None = None, norm: Normalize | None = None, infer_intervals: bool | None = None, center: float | None = None, levels: ArrayLike | None = None, robust: bool | None = None, colors: str | ArrayLike | None = None, extend: ExtendOptions = None, cmap: str | Colormap | None = None, **kwargs: Any, ) -> FacetGrid[Dataset]: ... @overload def streamplot( ds: Dataset, *args: Any, x: Hashable | None = None, y: Hashable | None = None, u: Hashable | None = None, v: Hashable | None = None, hue: Hashable | None = None, hue_style: HueStyleOptions = None, col: Hashable | None = None, row: Hashable, # wrap -> FacetGrid ax: Axes | None = None, figsize: Iterable[float] | None = None, size: float | None = None, col_wrap: int | None = None, sharex: bool = True, sharey: bool = True, aspect: AspectOptions = None, subplot_kws: dict[str, Any] | None = None, add_guide: bool | None = None, cbar_kwargs: dict[str, Any] | None = None, cbar_ax: Axes | None = None, vmin: float | None = None, vmax: float | None = None, norm: Normalize | None = None, infer_intervals: bool | None = None, center: float | None = None, levels: ArrayLike | None = None, robust: bool | None = None, colors: str | ArrayLike | None = None, extend: ExtendOptions = None, cmap: str | Colormap | None = None, **kwargs: Any, ) -> FacetGrid[Dataset]: ... @_dsplot def streamplot( ds: Dataset, x: Hashable, y: Hashable, ax: Axes, u: Hashable, v: Hashable, **kwargs: Any, ) -> LineCollection: """Plot streamlines of Dataset variables. Wraps :py:func:`matplotlib:matplotlib.pyplot.streamplot`. """ import matplotlib as mpl if x is None or y is None or u is None or v is None: raise ValueError("Must specify x, y, u, v for streamplot plots.") # Matplotlib's streamplot has strong restrictions on what x and y can be, so need to # get arrays transposed the 'right' way around. 'x' cannot vary within 'rows', so # the dimension of x must be the second dimension. 'y' cannot vary with 'columns' so # the dimension of y must be the first dimension. If x and y are both 2d, assume the # user has got them right already. xdim = ds[x].dims[0] if len(ds[x].dims) == 1 else None ydim = ds[y].dims[0] if len(ds[y].dims) == 1 else None if xdim is not None and ydim is None: ydims = set(ds[y].dims) - {xdim} if len(ydims) == 1: ydim = next(iter(ydims)) if ydim is not None and xdim is None: xdims = set(ds[x].dims) - {ydim} if len(xdims) == 1: xdim = next(iter(xdims)) dx, dy, du, dv = broadcast(ds[x], ds[y], ds[u], ds[v]) if xdim is not None and ydim is not None: # Need to ensure the arrays are transposed correctly dx = dx.transpose(ydim, xdim) dy = dy.transpose(ydim, xdim) du = du.transpose(ydim, xdim) dv = dv.transpose(ydim, xdim) args = [dx.values, dy.values, du.values, dv.values] hue = kwargs.pop("hue") cmap_params = kwargs.pop("cmap_params") if hue: kwargs["color"] = ds[hue].values # TODO: Fix this by always returning a norm with vmin, vmax in cmap_params if not cmap_params["norm"]: cmap_params["norm"] = mpl.colors.Normalize( cmap_params.pop("vmin"), cmap_params.pop("vmax") ) kwargs.pop("hue_style") hdl = ax.streamplot(*args, **kwargs, **cmap_params) # Return .lines so colorbar creation works properly return hdl.lines F = TypeVar("F", bound=Callable) def _update_doc_to_dataset(dataarray_plotfunc: Callable) -> Callable[[F], F]: """ Add a common docstring by re-using the DataArray one. TODO: Reduce code duplication. * The goal is to reduce code duplication by moving all Dataset specific plots to the DataArray side and use this thin wrapper to handle the conversion between Dataset and DataArray. * Improve docstring handling, maybe reword the DataArray versions to explain Datasets better. Parameters ---------- dataarray_plotfunc : Callable Function that returns a finished plot primitive. """ # Build on the original docstring da_doc = dataarray_plotfunc.__doc__ if da_doc is None: raise NotImplementedError("DataArray plot method requires a docstring") da_str = """ Parameters ---------- darray : DataArray """ ds_str = """ The `y` DataArray will be used as base, any other variables are added as coords. Parameters ---------- ds : Dataset """ # TODO: improve this? if da_str in da_doc: ds_doc = da_doc.replace(da_str, ds_str).replace("darray", "ds") else: ds_doc = da_doc @functools.wraps(dataarray_plotfunc) def wrapper(dataset_plotfunc: F) -> F: dataset_plotfunc.__doc__ = ds_doc return dataset_plotfunc return wrapper def _normalize_args( plotmethod: str, args: tuple[Any, ...], kwargs: dict[str, Any] ) -> dict[str, Any]: from xarray.core.dataarray import DataArray # Determine positional arguments keyword by inspecting the # signature of the plotmethod: locals_ = dict( inspect.signature(getattr(DataArray().plot, plotmethod)) .bind(*args, **kwargs) .arguments.items() ) locals_.update(locals_.pop("kwargs", {})) return locals_ def _temp_dataarray(ds: Dataset, y: Hashable, locals_: dict[str, Any]) -> DataArray: """Create a temporary datarray with extra coords.""" from xarray.core.dataarray import DataArray # Base coords: coords = dict(ds.coords) # Add extra coords to the DataArray from valid kwargs, if using all # kwargs there is a risk that we add unneccessary dataarrays as # coords straining RAM further for example: # ds.both and extend="both" would add ds.both to the coords: valid_coord_kwargs = {"x", "z", "markersize", "hue", "row", "col", "u", "v"} coord_kwargs = locals_.keys() & valid_coord_kwargs for k in coord_kwargs: key = locals_[k] if ds.data_vars.get(key) is not None: coords[key] = ds[key] # The dataarray has to include all the dims. Broadcast to that shape # and add the additional coords: _y = ds[y].broadcast_like(ds) return DataArray(_y, coords=coords) @overload def scatter( ds: Dataset, *args: Any, x: Hashable | None = None, y: Hashable | None = None, z: Hashable | None = None, hue: Hashable | None = None, hue_style: HueStyleOptions = None, markersize: Hashable | None = None, linewidth: Hashable | None = None, figsize: Iterable[float] | None = None, size: float | None = None, aspect: float | None = None, ax: Axes | None = None, row: None = None, # no wrap -> primitive col: None = None, # no wrap -> primitive col_wrap: int | None = None, xincrease: bool | None = True, yincrease: bool | None = True, add_legend: bool | None = None, add_colorbar: bool | None = None, add_labels: bool | Iterable[bool] = True, add_title: bool = True, subplot_kws: dict[str, Any] | None = None, xscale: ScaleOptions = None, yscale: ScaleOptions = None, xticks: ArrayLike | None = None, yticks: ArrayLike | None = None, xlim: ArrayLike | None = None, ylim: ArrayLike | None = None, cmap: str | Colormap | None = None, vmin: float | None = None, vmax: float | None = None, norm: Normalize | None = None, extend: ExtendOptions = None, levels: ArrayLike | None = None, **kwargs: Any, ) -> PathCollection: ... @overload def scatter( ds: Dataset, *args: Any, x: Hashable | None = None, y: Hashable | None = None, z: Hashable | None = None, hue: Hashable | None = None, hue_style: HueStyleOptions = None, markersize: Hashable | None = None, linewidth: Hashable | None = None, figsize: Iterable[float] | None = None, size: float | None = None, aspect: float | None = None, ax: Axes | None = None, row: Hashable | None = None, col: Hashable, # wrap -> FacetGrid col_wrap: int | None = None, xincrease: bool | None = True, yincrease: bool | None = True, add_legend: bool | None = None, add_colorbar: bool | None = None, add_labels: bool | Iterable[bool] = True, add_title: bool = True, subplot_kws: dict[str, Any] | None = None, xscale: ScaleOptions = None, yscale: ScaleOptions = None, xticks: ArrayLike | None = None, yticks: ArrayLike | None = None, xlim: ArrayLike | None = None, ylim: ArrayLike | None = None, cmap: str | Colormap | None = None, vmin: float | None = None, vmax: float | None = None, norm: Normalize | None = None, extend: ExtendOptions = None, levels: ArrayLike | None = None, **kwargs: Any, ) -> FacetGrid[DataArray]: ... @overload def scatter( ds: Dataset, *args: Any, x: Hashable | None = None, y: Hashable | None = None, z: Hashable | None = None, hue: Hashable | None = None, hue_style: HueStyleOptions = None, markersize: Hashable | None = None, linewidth: Hashable | None = None, figsize: Iterable[float] | None = None, size: float | None = None, aspect: float | None = None, ax: Axes | None = None, row: Hashable, # wrap -> FacetGrid col: Hashable | None = None, col_wrap: int | None = None, xincrease: bool | None = True, yincrease: bool | None = True, add_legend: bool | None = None, add_colorbar: bool | None = None, add_labels: bool | Iterable[bool] = True, add_title: bool = True, subplot_kws: dict[str, Any] | None = None, xscale: ScaleOptions = None, yscale: ScaleOptions = None, xticks: ArrayLike | None = None, yticks: ArrayLike | None = None, xlim: ArrayLike | None = None, ylim: ArrayLike | None = None, cmap: str | Colormap | None = None, vmin: float | None = None, vmax: float | None = None, norm: Normalize | None = None, extend: ExtendOptions = None, levels: ArrayLike | None = None, **kwargs: Any, ) -> FacetGrid[DataArray]: ... @_update_doc_to_dataset(dataarray_plot.scatter) def scatter( ds: Dataset, *args: Any, x: Hashable | None = None, y: Hashable | None = None, z: Hashable | None = None, hue: Hashable | None = None, hue_style: HueStyleOptions = None, markersize: Hashable | None = None, linewidth: Hashable | None = None, figsize: Iterable[float] | None = None, size: float | None = None, aspect: float | None = None, ax: Axes | None = None, row: Hashable | None = None, col: Hashable | None = None, col_wrap: int | None = None, xincrease: bool | None = True, yincrease: bool | None = True, add_legend: bool | None = None, add_colorbar: bool | None = None, add_labels: bool | Iterable[bool] = True, add_title: bool = True, subplot_kws: dict[str, Any] | None = None, xscale: ScaleOptions = None, yscale: ScaleOptions = None, xticks: ArrayLike | None = None, yticks: ArrayLike | None = None, xlim: ArrayLike | None = None, ylim: ArrayLike | None = None, cmap: str | Colormap | None = None, vmin: float | None = None, vmax: float | None = None, norm: Normalize | None = None, extend: ExtendOptions = None, levels: ArrayLike | None = None, **kwargs: Any, ) -> PathCollection | FacetGrid[DataArray]: """Scatter plot Dataset data variables against each other.""" locals_ = locals() del locals_["ds"] locals_.update(locals_.pop("kwargs", {})) da = _temp_dataarray(ds, y, locals_) return da.plot.scatter(*locals_.pop("args", ()), **locals_)