from __future__ import annotations import functools import itertools import warnings from collections.abc import Callable, Hashable, Iterable, MutableMapping from typing import TYPE_CHECKING, Any, Generic, Literal, TypeVar, cast import numpy as np from xarray.core.formatting import format_item from xarray.core.types import HueStyleOptions, T_DataArrayOrSet from xarray.plot.utils import ( _LINEWIDTH_RANGE, _MARKERSIZE_RANGE, _add_legend, _determine_guide, _get_nice_quiver_magnitude, _guess_coords_to_plot, _infer_xy_labels, _Normalize, _parse_size, _process_cmap_cbar_kwargs, label_from_attrs, ) if TYPE_CHECKING: from matplotlib.axes import Axes from matplotlib.cm import ScalarMappable from matplotlib.colorbar import Colorbar from matplotlib.figure import Figure from matplotlib.legend import Legend from matplotlib.quiver import QuiverKey from matplotlib.text import Annotation from xarray.core.dataarray import DataArray # Overrides axes.labelsize, xtick.major.size, ytick.major.size # from mpl.rcParams _FONTSIZE = "small" # For major ticks on x, y axes _NTICKS = 5 def _nicetitle(coord, value, maxchar, template): """ Put coord, value in template and truncate at maxchar """ prettyvalue = format_item(value, quote_strings=False) title = template.format(coord=coord, value=prettyvalue) if len(title) > maxchar: title = title[: (maxchar - 3)] + "..." return title T_FacetGrid = TypeVar("T_FacetGrid", bound="FacetGrid") class FacetGrid(Generic[T_DataArrayOrSet]): """ Initialize the Matplotlib figure and FacetGrid object. The :class:`FacetGrid` is an object that links a xarray DataArray to a Matplotlib figure with a particular structure. In particular, :class:`FacetGrid` is used to draw plots with multiple axes, where each axes shows the same relationship conditioned on different levels of some dimension. It's possible to condition on up to two variables by assigning variables to the rows and columns of the grid. The general approach to plotting here is called "small multiples", where the same kind of plot is repeated multiple times, and the specific use of small multiples to display the same relationship conditioned on one or more other variables is often called a "trellis plot". The basic workflow is to initialize the :class:`FacetGrid` object with the DataArray and the variable names that are used to structure the grid. Then plotting functions can be applied to each subset by calling :meth:`FacetGrid.map_dataarray` or :meth:`FacetGrid.map`. Attributes ---------- axs : ndarray of matplotlib.axes.Axes Array containing axes in corresponding position, as returned from :py:func:`matplotlib.pyplot.subplots`. col_labels : list of matplotlib.text.Annotation Column titles. row_labels : list of matplotlib.text.Annotation Row titles. fig : matplotlib.figure.Figure The figure containing all the axes. name_dicts : ndarray of dict Array containing dictionaries mapping coordinate names to values. ``None`` is used as a sentinel value for axes that should remain empty, i.e., sometimes the rightmost grid positions in the bottom row. """ data: T_DataArrayOrSet name_dicts: np.ndarray fig: Figure axs: np.ndarray row_names: list[np.ndarray] col_names: list[np.ndarray] figlegend: Legend | None quiverkey: QuiverKey | None cbar: Colorbar | None _single_group: bool | Hashable _nrow: int _row_var: Hashable | None _ncol: int _col_var: Hashable | None _col_wrap: int | None row_labels: list[Annotation | None] col_labels: list[Annotation | None] _x_var: None _y_var: None _hue_var: DataArray | None _cmap_extend: Any | None _mappables: list[ScalarMappable] _finalized: bool def __init__( self, data: T_DataArrayOrSet, col: Hashable | None = None, row: Hashable | None = None, col_wrap: int | None = None, sharex: bool = True, sharey: bool = True, figsize: Iterable[float] | None = None, aspect: float = 1, size: float = 3, subplot_kws: dict[str, Any] | None = None, ) -> None: """ Parameters ---------- data : DataArray or Dataset DataArray or Dataset to be plotted. row, col : str Dimension names that define subsets of the data, which will be drawn on separate facets in the grid. col_wrap : int, optional "Wrap" the grid the for the column variable after this number of columns, adding rows if ``col_wrap`` is less than the number of facets. sharex : bool, optional If true, the facets will share *x* axes. sharey : bool, optional If true, the facets will share *y* axes. figsize : Iterable of float or None, optional A tuple (width, height) of the figure in inches. If set, overrides ``size`` and ``aspect``. aspect : scalar, default: 1 Aspect ratio of each facet, so that ``aspect * size`` gives the width of each facet in inches. size : scalar, default: 3 Height (in inches) of each facet. See also: ``aspect``. subplot_kws : dict, optional Dictionary of keyword arguments for Matplotlib subplots (:py:func:`matplotlib.pyplot.subplots`). """ import matplotlib.pyplot as plt # Handle corner case of nonunique coordinates rep_col = col is not None and not data[col].to_index().is_unique rep_row = row is not None and not data[row].to_index().is_unique if rep_col or rep_row: raise ValueError( "Coordinates used for faceting cannot " "contain repeated (nonunique) values." ) # single_group is the grouping variable, if there is exactly one single_group: bool | Hashable if col and row: single_group = False nrow = len(data[row]) ncol = len(data[col]) nfacet = nrow * ncol if col_wrap is not None: warnings.warn( "Ignoring col_wrap since both col and row were passed", stacklevel=2 ) elif row and not col: single_group = row elif not row and col: single_group = col else: raise ValueError("Pass a coordinate name as an argument for row or col") # Compute grid shape if single_group: nfacet = len(data[single_group]) if col: # idea - could add heuristic for nice shapes like 3x4 ncol = nfacet if row: ncol = 1 if col_wrap is not None: # Overrides previous settings ncol = col_wrap nrow = int(np.ceil(nfacet / ncol)) # Set the subplot kwargs subplot_kws = {} if subplot_kws is None else subplot_kws if figsize is None: # Calculate the base figure size with extra horizontal space for a # colorbar cbar_space = 1 figsize = (ncol * size * aspect + cbar_space, nrow * size) fig, axs = plt.subplots( nrow, ncol, sharex=sharex, sharey=sharey, squeeze=False, figsize=figsize, subplot_kw=subplot_kws, ) # Set up the lists of names for the row and column facet variables col_names = list(data[col].to_numpy()) if col else [] row_names = list(data[row].to_numpy()) if row else [] if single_group: full: list[dict[Hashable, Any] | None] = [ {single_group: x} for x in data[single_group].to_numpy() ] empty: list[dict[Hashable, Any] | None] = [ None for x in range(nrow * ncol - len(full)) ] name_dict_list = full + empty else: rowcols = itertools.product(row_names, col_names) name_dict_list = [{row: r, col: c} for r, c in rowcols] name_dicts = np.array(name_dict_list).reshape(nrow, ncol) # Set up the class attributes # --------------------------- # First the public API self.data = data self.name_dicts = name_dicts self.fig = fig self.axs = axs self.row_names = row_names self.col_names = col_names # guides self.figlegend = None self.quiverkey = None self.cbar = None # Next the private variables self._single_group = single_group self._nrow = nrow self._row_var = row self._ncol = ncol self._col_var = col self._col_wrap = col_wrap self.row_labels = [None] * nrow self.col_labels = [None] * ncol self._x_var = None self._y_var = None self._hue_var = None self._cmap_extend = None self._mappables = [] self._finalized = False @property def axes(self) -> np.ndarray: warnings.warn( ( "self.axes is deprecated since 2022.11 in order to align with " "matplotlibs plt.subplots, use self.axs instead." ), DeprecationWarning, stacklevel=2, ) return self.axs @axes.setter def axes(self, axs: np.ndarray) -> None: warnings.warn( ( "self.axes is deprecated since 2022.11 in order to align with " "matplotlibs plt.subplots, use self.axs instead." ), DeprecationWarning, stacklevel=2, ) self.axs = axs @property def _left_axes(self) -> np.ndarray: return self.axs[:, 0] @property def _bottom_axes(self) -> np.ndarray: return self.axs[-1, :] def map_dataarray( self: T_FacetGrid, func: Callable, x: Hashable | None, y: Hashable | None, **kwargs: Any, ) -> T_FacetGrid: """ Apply a plotting function to a 2d facet's subset of the data. This is more convenient and less general than ``FacetGrid.map`` Parameters ---------- func : callable A plotting function with the same signature as a 2d xarray plotting method such as `xarray.plot.imshow` x, y : string Names of the coordinates to plot on x, y axes **kwargs additional keyword arguments to func Returns ------- self : FacetGrid object """ if kwargs.get("cbar_ax") is not None: raise ValueError("cbar_ax not supported by FacetGrid.") cmap_params, cbar_kwargs = _process_cmap_cbar_kwargs( func, self.data.to_numpy(), **kwargs ) self._cmap_extend = cmap_params.get("extend") # Order is important func_kwargs = { k: v for k, v in kwargs.items() if k not in {"cmap", "colors", "cbar_kwargs", "levels"} } func_kwargs.update(cmap_params) func_kwargs["add_colorbar"] = False if func.__name__ != "surface": func_kwargs["add_labels"] = False # Get x, y labels for the first subplot x, y = _infer_xy_labels( darray=self.data.loc[self.name_dicts.flat[0]], x=x, y=y, imshow=func.__name__ == "imshow", rgb=kwargs.get("rgb"), ) for d, ax in zip(self.name_dicts.flat, self.axs.flat, strict=True): # None is the sentinel value if d is not None: subset = self.data.loc[d] mappable = func( subset, x=x, y=y, ax=ax, **func_kwargs, _is_facetgrid=True ) self._mappables.append(mappable) self._finalize_grid(x, y) if kwargs.get("add_colorbar", True): self.add_colorbar(**cbar_kwargs) return self def map_plot1d( self: T_FacetGrid, func: Callable, x: Hashable | None, y: Hashable | None, *, z: Hashable | None = None, hue: Hashable | None = None, markersize: Hashable | None = None, linewidth: Hashable | None = None, **kwargs: Any, ) -> T_FacetGrid: """ Apply a plotting function to a 1d facet's subset of the data. This is more convenient and less general than ``FacetGrid.map`` Parameters ---------- func : A plotting function with the same signature as a 1d xarray plotting method such as `xarray.plot.scatter` x, y : Names of the coordinates to plot on x, y axes **kwargs additional keyword arguments to func Returns ------- self : FacetGrid object """ # Copy data to allow converting categoricals to integers and storing # them in self.data. It is not possible to copy in the init # unfortunately as there are tests that relies on self.data being # mutable (test_names_appear_somewhere()). Maybe something to deprecate # not sure how much that is used outside these tests. self.data = self.data.copy() if kwargs.get("cbar_ax") is not None: raise ValueError("cbar_ax not supported by FacetGrid.") if func.__name__ == "scatter": size_ = kwargs.pop("_size", markersize) size_r = _MARKERSIZE_RANGE else: size_ = kwargs.pop("_size", linewidth) size_r = _LINEWIDTH_RANGE # Guess what coords to use if some of the values in coords_to_plot are None: coords_to_plot: MutableMapping[str, Hashable | None] = dict( x=x, z=z, hue=hue, size=size_ ) coords_to_plot = _guess_coords_to_plot(self.data, coords_to_plot, kwargs) # Handle hues: hue = coords_to_plot["hue"] hueplt = self.data.coords[hue] if hue else None # TODO: _infer_line_data2 ? hueplt_norm = _Normalize(hueplt) self._hue_var = hueplt cbar_kwargs = kwargs.pop("cbar_kwargs", {}) if hueplt_norm.data is not None: if not hueplt_norm.data_is_numeric: # TODO: Ticks seems a little too hardcoded, since it will always # show all the values. But maybe it's ok, since plotting hundreds # of categorical data isn't that meaningful anyway. cbar_kwargs.update(format=hueplt_norm.format, ticks=hueplt_norm.ticks) kwargs.update(levels=hueplt_norm.levels) cmap_params, cbar_kwargs = _process_cmap_cbar_kwargs( func, cast("DataArray", hueplt_norm.values).data, cbar_kwargs=cbar_kwargs, **kwargs, ) self._cmap_extend = cmap_params.get("extend") else: cmap_params = {} # Handle sizes: size_ = coords_to_plot["size"] sizeplt = self.data.coords[size_] if size_ else None sizeplt_norm = _Normalize(data=sizeplt, width=size_r) if sizeplt_norm.data is not None: self.data[size_] = sizeplt_norm.values # Add kwargs that are sent to the plotting function, # order is important ??? func_kwargs = { k: v for k, v in kwargs.items() if k not in {"cmap", "colors", "cbar_kwargs", "levels"} } func_kwargs.update(cmap_params) # Annotations will be handled later, skip those parts in the plotfunc: func_kwargs["add_colorbar"] = False func_kwargs["add_legend"] = False func_kwargs["add_title"] = False add_labels_ = np.zeros(self.axs.shape + (3,), dtype=bool) if kwargs.get("z") is not None: # 3d plots looks better with all labels. 3d plots can't sharex either so it # is easy to get lost while rotating the plots: add_labels_[:] = True else: # Subplots should have labels on the left and bottom edges only: add_labels_[-1, :, 0] = True # x add_labels_[:, 0, 1] = True # y # add_labels_[:, :, 2] = True # z # Set up the lists of names for the row and column facet variables: if self._single_group: full = tuple( {self._single_group: x} for x in range(self.data[self._single_group].size) ) empty = tuple(None for x in range(self._nrow * self._ncol - len(full))) name_d = full + empty else: rowcols = itertools.product( range(self.data[self._row_var].size), range(self.data[self._col_var].size), ) name_d = tuple({self._row_var: r, self._col_var: c} for r, c in rowcols) name_dicts = np.array(name_d).reshape(self._nrow, self._ncol) # Plot the data for each subplot: for add_lbls, d, ax in zip( add_labels_.reshape((self.axs.size, -1)), name_dicts.flat, self.axs.flat, strict=True, ): func_kwargs["add_labels"] = add_lbls # None is the sentinel value if d is not None: subset = self.data.isel(d) mappable = func( subset, x=x, y=y, ax=ax, hue=hue, _size=size_, **func_kwargs, _is_facetgrid=True, ) self._mappables.append(mappable) # Add titles and some touch ups: self._finalize_grid() self._set_lims() add_colorbar, add_legend = _determine_guide( hueplt_norm, sizeplt_norm, kwargs.get("add_colorbar"), kwargs.get("add_legend"), # kwargs.get("add_guide", None), # kwargs.get("hue_style", None), ) if add_legend: use_legend_elements = False if func.__name__ == "hist" else True if use_legend_elements: self.add_legend( use_legend_elements=use_legend_elements, hueplt_norm=hueplt_norm if not add_colorbar else _Normalize(None), sizeplt_norm=sizeplt_norm, primitive=self._mappables, legend_ax=self.fig, plotfunc=func.__name__, ) else: self.add_legend(use_legend_elements=use_legend_elements) if add_colorbar: # Colorbar is after legend so it correctly fits the plot: if "label" not in cbar_kwargs: cbar_kwargs["label"] = label_from_attrs(hueplt_norm.data) self.add_colorbar(**cbar_kwargs) return self def map_dataarray_line( self: T_FacetGrid, func: Callable, x: Hashable | None, y: Hashable | None, hue: Hashable | None, add_legend: bool = True, _labels=None, **kwargs: Any, ) -> T_FacetGrid: from xarray.plot.dataarray_plot import _infer_line_data for d, ax in zip(self.name_dicts.flat, self.axs.flat, strict=True): # None is the sentinel value if d is not None: subset = self.data.loc[d] mappable = func( subset, x=x, y=y, ax=ax, hue=hue, add_legend=False, _labels=False, **kwargs, ) self._mappables.append(mappable) xplt, yplt, hueplt, huelabel = _infer_line_data( darray=self.data.loc[self.name_dicts.flat[0]], x=x, y=y, hue=hue ) xlabel = label_from_attrs(xplt) ylabel = label_from_attrs(yplt) self._hue_var = hueplt self._finalize_grid(xlabel, ylabel) if add_legend and hueplt is not None and huelabel is not None: self.add_legend(label=huelabel) return self def map_dataset( self: T_FacetGrid, func: Callable, x: Hashable | None = None, y: Hashable | None = None, hue: Hashable | None = None, hue_style: HueStyleOptions = None, add_guide: bool | None = None, **kwargs: Any, ) -> T_FacetGrid: from xarray.plot.dataset_plot import _infer_meta_data kwargs["add_guide"] = False if kwargs.get("markersize"): kwargs["size_mapping"] = _parse_size( self.data[kwargs["markersize"]], kwargs.pop("size_norm", None) ) meta_data = _infer_meta_data( self.data, x, y, hue, hue_style, add_guide, funcname=func.__name__ ) kwargs["meta_data"] = meta_data if hue and meta_data["hue_style"] == "continuous": cmap_params, cbar_kwargs = _process_cmap_cbar_kwargs( func, self.data[hue].to_numpy(), **kwargs ) kwargs["meta_data"]["cmap_params"] = cmap_params kwargs["meta_data"]["cbar_kwargs"] = cbar_kwargs kwargs["_is_facetgrid"] = True if func.__name__ == "quiver" and "scale" not in kwargs: raise ValueError("Please provide scale.") # TODO: come up with an algorithm for reasonable scale choice for d, ax in zip(self.name_dicts.flat, self.axs.flat, strict=True): # None is the sentinel value if d is not None: subset = self.data.loc[d] maybe_mappable = func( ds=subset, x=x, y=y, hue=hue, hue_style=hue_style, ax=ax, **kwargs ) # TODO: this is needed to get legends to work. # but maybe_mappable is a list in that case :/ self._mappables.append(maybe_mappable) self._finalize_grid(meta_data["xlabel"], meta_data["ylabel"]) if hue: hue_label = meta_data.pop("hue_label", None) self._hue_label = hue_label if meta_data["add_legend"]: self._hue_var = meta_data["hue"] self.add_legend(label=hue_label) elif meta_data["add_colorbar"]: self.add_colorbar(label=hue_label, **cbar_kwargs) if meta_data["add_quiverkey"]: self.add_quiverkey(kwargs["u"], kwargs["v"]) return self def _finalize_grid(self, *axlabels: Hashable) -> None: """Finalize the annotations and layout.""" if not self._finalized: self.set_axis_labels(*axlabels) self.set_titles() self.fig.tight_layout() for ax, namedict in zip(self.axs.flat, self.name_dicts.flat, strict=True): if namedict is None: ax.set_visible(False) self._finalized = True def _adjust_fig_for_guide(self, guide) -> None: # Draw the plot to set the bounding boxes correctly if hasattr(self.fig.canvas, "get_renderer"): renderer = self.fig.canvas.get_renderer() else: raise RuntimeError("MPL backend has no renderer") self.fig.draw(renderer) # Calculate and set the new width of the figure so the legend fits guide_width = guide.get_window_extent(renderer).width / self.fig.dpi figure_width = self.fig.get_figwidth() total_width = figure_width + guide_width self.fig.set_figwidth(total_width) # Draw the plot again to get the new transformations self.fig.draw(renderer) # Now calculate how much space we need on the right side guide_width = guide.get_window_extent(renderer).width / self.fig.dpi space_needed = guide_width / total_width + 0.02 # margin = .01 # _space_needed = margin + space_needed right = 1 - space_needed # Place the subplot axes to give space for the legend self.fig.subplots_adjust(right=right) def add_legend( self, *, label: str | None = None, use_legend_elements: bool = False, **kwargs: Any, ) -> None: if use_legend_elements: self.figlegend = _add_legend(**kwargs) else: assert self._hue_var is not None self.figlegend = self.fig.legend( handles=self._mappables[-1], labels=list(self._hue_var.to_numpy()), title=label if label is not None else label_from_attrs(self._hue_var), loc=kwargs.pop("loc", "center right"), **kwargs, ) self._adjust_fig_for_guide(self.figlegend) def add_colorbar(self, **kwargs: Any) -> None: """Draw a colorbar.""" kwargs = kwargs.copy() if self._cmap_extend is not None: kwargs.setdefault("extend", self._cmap_extend) # dont pass extend as kwarg if it is in the mappable if hasattr(self._mappables[-1], "extend"): kwargs.pop("extend", None) if "label" not in kwargs: from xarray import DataArray assert isinstance(self.data, DataArray) kwargs.setdefault("label", label_from_attrs(self.data)) self.cbar = self.fig.colorbar( self._mappables[-1], ax=list(self.axs.flat), **kwargs ) def add_quiverkey(self, u: Hashable, v: Hashable, **kwargs: Any) -> None: kwargs = kwargs.copy() magnitude = _get_nice_quiver_magnitude(self.data[u], self.data[v]) units = self.data[u].attrs.get("units", "") self.quiverkey = self.axs.flat[-1].quiverkey( self._mappables[-1], X=0.8, Y=0.9, U=magnitude, label=f"{magnitude}\n{units}", labelpos="E", coordinates="figure", ) # TODO: does not work because self.quiverkey.get_window_extent(renderer) = 0 # https://github.com/matplotlib/matplotlib/issues/18530 # self._adjust_fig_for_guide(self.quiverkey.text) def _get_largest_lims(self) -> dict[str, tuple[float, float]]: """ Get largest limits in the facetgrid. Returns ------- lims_largest : dict[str, tuple[float, float]] Dictionary with the largest limits along each axis. Examples -------- >>> ds = xr.tutorial.scatter_example_dataset(seed=42) >>> fg = ds.plot.scatter(x="A", y="B", hue="y", row="x", col="w") >>> round(fg._get_largest_lims()["x"][0], 3) np.float64(-0.334) """ lims_largest: dict[str, tuple[float, float]] = dict( x=(np.inf, -np.inf), y=(np.inf, -np.inf), z=(np.inf, -np.inf) ) for axis in ("x", "y", "z"): # Find the plot with the largest xlim values: lower, upper = lims_largest[axis] for ax in self.axs.flat: get_lim: None | Callable[[], tuple[float, float]] = getattr( ax, f"get_{axis}lim", None ) if get_lim: lower_new, upper_new = get_lim() lower, upper = (min(lower, lower_new), max(upper, upper_new)) lims_largest[axis] = (lower, upper) return lims_largest def _set_lims( self, x: tuple[float, float] | None = None, y: tuple[float, float] | None = None, z: tuple[float, float] | None = None, ) -> None: """ Set the same limits for all the subplots in the facetgrid. Parameters ---------- x : tuple[float, float] or None, optional x axis limits. y : tuple[float, float] or None, optional y axis limits. z : tuple[float, float] or None, optional z axis limits. Examples -------- >>> ds = xr.tutorial.scatter_example_dataset(seed=42) >>> fg = ds.plot.scatter(x="A", y="B", hue="y", row="x", col="w") >>> fg._set_lims(x=(-0.3, 0.3), y=(0, 2), z=(0, 4)) >>> fg.axs[0, 0].get_xlim(), fg.axs[0, 0].get_ylim() ((np.float64(-0.3), np.float64(0.3)), (np.float64(0.0), np.float64(2.0))) """ lims_largest = self._get_largest_lims() # Set limits: for ax in self.axs.flat: for (axis, data_limit), parameter_limit in zip( lims_largest.items(), (x, y, z), strict=True ): set_lim = getattr(ax, f"set_{axis}lim", None) if set_lim: set_lim(data_limit if parameter_limit is None else parameter_limit) def set_axis_labels(self, *axlabels: Hashable) -> None: """Set axis labels on the left column and bottom row of the grid.""" from xarray.core.dataarray import DataArray for var, axis in zip(axlabels, ["x", "y", "z"], strict=False): if var is not None: if isinstance(var, DataArray): getattr(self, f"set_{axis}labels")(label_from_attrs(var)) else: getattr(self, f"set_{axis}labels")(str(var)) def _set_labels( self, axis: str, axes: Iterable, label: str | None = None, **kwargs ) -> None: if label is None: label = label_from_attrs(self.data[getattr(self, f"_{axis}_var")]) for ax in axes: getattr(ax, f"set_{axis}label")(label, **kwargs) def set_xlabels(self, label: None | str = None, **kwargs: Any) -> None: """Label the x axis on the bottom row of the grid.""" self._set_labels("x", self._bottom_axes, label, **kwargs) def set_ylabels(self, label: None | str = None, **kwargs: Any) -> None: """Label the y axis on the left column of the grid.""" self._set_labels("y", self._left_axes, label, **kwargs) def set_zlabels(self, label: None | str = None, **kwargs: Any) -> None: """Label the z axis.""" self._set_labels("z", self._left_axes, label, **kwargs) def set_titles( self, template: str = "{coord} = {value}", maxchar: int = 30, size=None, **kwargs, ) -> None: """ Draw titles either above each facet or on the grid margins. Parameters ---------- template : str, default: "{coord} = {value}" Template for plot titles containing {coord} and {value} maxchar : int, default: 30 Truncate titles at maxchar **kwargs : keyword args additional arguments to matplotlib.text Returns ------- self: FacetGrid object """ import matplotlib as mpl if size is None: size = mpl.rcParams["axes.labelsize"] nicetitle = functools.partial(_nicetitle, maxchar=maxchar, template=template) if self._single_group: for d, ax in zip(self.name_dicts.flat, self.axs.flat, strict=True): # Only label the ones with data if d is not None: coord, value = list(d.items()).pop() title = nicetitle(coord, value, maxchar=maxchar) ax.set_title(title, size=size, **kwargs) else: # The row titles on the right edge of the grid for index, (ax, row_name, handle) in enumerate( zip(self.axs[:, -1], self.row_names, self.row_labels, strict=True) ): title = nicetitle(coord=self._row_var, value=row_name, maxchar=maxchar) if not handle: self.row_labels[index] = ax.annotate( title, xy=(1.02, 0.5), xycoords="axes fraction", rotation=270, ha="left", va="center", **kwargs, ) else: handle.set_text(title) handle.update(kwargs) # The column titles on the top row for index, (ax, col_name, handle) in enumerate( zip(self.axs[0, :], self.col_names, self.col_labels, strict=True) ): title = nicetitle(coord=self._col_var, value=col_name, maxchar=maxchar) if not handle: self.col_labels[index] = ax.set_title(title, size=size, **kwargs) else: handle.set_text(title) handle.update(kwargs) def set_ticks( self, max_xticks: int = _NTICKS, max_yticks: int = _NTICKS, fontsize: str | int = _FONTSIZE, ) -> None: """ Set and control tick behavior. Parameters ---------- max_xticks, max_yticks : int, optional Maximum number of labeled ticks to plot on x, y axes fontsize : string or int Font size as used by matplotlib text Returns ------- self : FacetGrid object """ from matplotlib.ticker import MaxNLocator # Both are necessary x_major_locator = MaxNLocator(nbins=max_xticks) y_major_locator = MaxNLocator(nbins=max_yticks) for ax in self.axs.flat: ax.xaxis.set_major_locator(x_major_locator) ax.yaxis.set_major_locator(y_major_locator) for tick in itertools.chain( ax.xaxis.get_major_ticks(), ax.yaxis.get_major_ticks() ): tick.label1.set_fontsize(fontsize) def map( self: T_FacetGrid, func: Callable, *args: Hashable, **kwargs: Any ) -> T_FacetGrid: """ Apply a plotting function to each facet's subset of the data. Parameters ---------- func : callable A plotting function that takes data and keyword arguments. It must plot to the currently active matplotlib Axes and take a `color` keyword argument. If faceting on the `hue` dimension, it must also take a `label` keyword argument. *args : Hashable Column names in self.data that identify variables with data to plot. The data for each variable is passed to `func` in the order the variables are specified in the call. **kwargs : keyword arguments All keyword arguments are passed to the plotting function. Returns ------- self : FacetGrid object """ import matplotlib.pyplot as plt for ax, namedict in zip(self.axs.flat, self.name_dicts.flat, strict=True): if namedict is not None: data = self.data.loc[namedict] plt.sca(ax) innerargs = [data[a].to_numpy() for a in args] maybe_mappable = func(*innerargs, **kwargs) # TODO: better way to verify that an artist is mappable? # https://stackoverflow.com/questions/33023036/is-it-possible-to-detect-if-a-matplotlib-artist-is-a-mappable-suitable-for-use-w#33023522 if maybe_mappable and hasattr(maybe_mappable, "autoscale_None"): self._mappables.append(maybe_mappable) self._finalize_grid(*args[:2]) return self def _easy_facetgrid( data: T_DataArrayOrSet, plotfunc: Callable, kind: Literal["line", "dataarray", "dataset", "plot1d"], x: Hashable | None = None, y: Hashable | None = None, row: Hashable | None = None, col: Hashable | None = None, col_wrap: int | None = None, sharex: bool = True, sharey: bool = True, aspect: float | None = None, size: float | None = None, subplot_kws: dict[str, Any] | None = None, ax: Axes | None = None, figsize: Iterable[float] | None = None, **kwargs: Any, ) -> FacetGrid[T_DataArrayOrSet]: """ Convenience method to call xarray.plot.FacetGrid from 2d plotting methods kwargs are the arguments to 2d plotting method """ 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") if kwargs.get("z") is not None: # 3d plots doesn't support sharex, sharey, reset to mpl defaults: sharex = False sharey = False g = FacetGrid( data=data, col=col, row=row, col_wrap=col_wrap, sharex=sharex, sharey=sharey, figsize=figsize, aspect=aspect, size=size, subplot_kws=subplot_kws, ) if kind == "line": return g.map_dataarray_line(plotfunc, x, y, **kwargs) if kind == "dataarray": return g.map_dataarray(plotfunc, x, y, **kwargs) if kind == "plot1d": return g.map_plot1d(plotfunc, x, y, **kwargs) if kind == "dataset": return g.map_dataset(plotfunc, x, y, **kwargs) raise ValueError( f"kind must be one of `line`, `dataarray`, `dataset` or `plot1d`, got {kind}" )