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922 lines (825 loc) · 30.3 KB
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from __future__ import annotations
import functools
import inspect
import warnings
from collections.abc import Hashable, Iterable
from typing import TYPE_CHECKING, Any, Callable, 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 <matplotlib:users/explain/colors/colormaps>`
for more information.
If *seaborn* is installed, ``cmap`` may also be a
`seaborn color palette <https://seaborn.pydata.org/tutorial/color_palettes.html>`_.
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 inferred.
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( # type: ignore[misc,unused-ignore] # None is hashable :(
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( # type: ignore[misc,unused-ignore] # None is hashable :(
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)
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(
dx.values, dy.values, du.values, dv.values, **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 unnecessary 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( # type: ignore[misc,unused-ignore] # None is hashable :(
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_)