import functools import itertools import warnings import numpy as np from ..core.formatting import format_item from .utils import ( _infer_xy_labels, _process_cmap_cbar_kwargs, import_matplotlib_pyplot, label_from_attrs, ) # 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 class FacetGrid: """ 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 ore 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 ---------- axes : numpy object array Contains axes in corresponding position, as returned from plt.subplots col_labels : list list of :class:`matplotlib.text.Text` instances corresponding to column titles. row_labels : list list of :class:`matplotlib.text.Text` instances corresponding to row titles. fig : matplotlib.Figure The figure containing all the axes name_dicts : numpy object array Contains dictionaries mapping coordinate names to values. None is used as a sentinel value for axes which should remain empty, ie. sometimes the bottom right grid """ def __init__( self, data, col=None, row=None, col_wrap=None, sharex=True, sharey=True, figsize=None, aspect=1, size=3, subplot_kws=None, ): """ Parameters ---------- data : DataArray xarray DataArray to be plotted row, col : strings Dimesion names that define subsets of the data, which will be drawn on separate facets in the grid. col_wrap : int, optional "Wrap" the column variable at this width, so that the column facets sharex : bool, optional If true, the facets will share x axes sharey : bool, optional If true, the facets will share y axes figsize : tuple, optional A tuple (width, height) of the figure in inches. If set, overrides ``size`` and ``aspect``. aspect : scalar, optional Aspect ratio of each facet, so that ``aspect * size`` gives the width of each facet in inches size : scalar, optional Height (in inches) of each facet. See also: ``aspect`` subplot_kws : dict, optional Dictionary of keyword arguments for matplotlib subplots """ plt = import_matplotlib_pyplot() # 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 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") 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, axes = 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].values) if col else [] row_names = list(data[row].values) if row else [] if single_group: full = [{single_group: x} for x in data[single_group].values] empty = [None for x in range(nrow * ncol - len(full))] name_dicts = full + empty else: rowcols = itertools.product(row_names, col_names) name_dicts = [{row: r, col: c} for r, c in rowcols] name_dicts = np.array(name_dicts).reshape(nrow, ncol) # Set up the class attributes # --------------------------- # First the public API self.data = data self.name_dicts = name_dicts self.fig = fig self.axes = axes self.row_names = row_names self.col_names = col_names self.figlegend = 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._cmap_extend = None self._mappables = [] self._finalized = False @property def _left_axes(self): return self.axes[:, 0] @property def _bottom_axes(self): return self.axes[-1, :] def map_dataarray(self, func, x, y, **kwargs): """ 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", None) is not None: raise ValueError("cbar_ax not supported by FacetGrid.") cmap_params, cbar_kwargs = _process_cmap_cbar_kwargs( func, self.data.values, **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.update({"add_colorbar": False, "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", None), ) for d, ax in zip(self.name_dicts.flat, self.axes.flat): # 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_dataarray_line( self, func, x, y, hue, add_legend=True, _labels=None, **kwargs ): from .plot import _infer_line_data for d, ax in zip(self.name_dicts.flat, self.axes.flat): # 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) _, _, hueplt, xlabel, ylabel, huelabel = _infer_line_data( darray=self.data.loc[self.name_dicts.flat[0]], x=x, y=y, hue=hue ) self._hue_var = hueplt self._hue_label = huelabel self._finalize_grid(xlabel, ylabel) if add_legend and hueplt is not None and huelabel is not None: self.add_legend() return self def map_dataset( self, func, x=None, y=None, hue=None, hue_style=None, add_guide=None, **kwargs ): from .dataset_plot import _infer_meta_data, _parse_size kwargs["add_guide"] = False kwargs["_is_facetgrid"] = True if kwargs.get("markersize", None): 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) 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].values, **kwargs ) kwargs["meta_data"]["cmap_params"] = cmap_params kwargs["meta_data"]["cbar_kwargs"] = cbar_kwargs for d, ax in zip(self.name_dicts.flat, self.axes.flat): # 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: self._hue_label = meta_data.pop("hue_label", None) if meta_data["add_legend"]: self._hue_var = meta_data["hue"] self.add_legend() elif meta_data["add_colorbar"]: self.add_colorbar(label=self._hue_label, **cbar_kwargs) return self def _finalize_grid(self, *axlabels): """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.axes.flat, self.name_dicts.flat): if namedict is None: ax.set_visible(False) self._finalized = True def add_legend(self, **kwargs): figlegend = self.fig.legend( handles=self._mappables[-1], labels=list(self._hue_var.values), title=self._hue_label, loc="center right", **kwargs, ) self.figlegend = figlegend # Draw the plot to set the bounding boxes correctly self.fig.draw(self.fig.canvas.get_renderer()) # Calculate and set the new width of the figure so the legend fits legend_width = figlegend.get_window_extent().width / self.fig.dpi figure_width = self.fig.get_figwidth() self.fig.set_figwidth(figure_width + legend_width) # Draw the plot again to get the new transformations self.fig.draw(self.fig.canvas.get_renderer()) # Now calculate how much space we need on the right side legend_width = figlegend.get_window_extent().width / self.fig.dpi space_needed = legend_width / (figure_width + legend_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_colorbar(self, **kwargs): """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: kwargs.setdefault("label", label_from_attrs(self.data)) self.cbar = self.fig.colorbar( self._mappables[-1], ax=list(self.axes.flat), **kwargs ) return self def set_axis_labels(self, x_var=None, y_var=None): """Set axis labels on the left column and bottom row of the grid.""" if x_var is not None: if x_var in self.data.coords: self._x_var = x_var self.set_xlabels(label_from_attrs(self.data[x_var])) else: # x_var is a string self.set_xlabels(x_var) if y_var is not None: if y_var in self.data.coords: self._y_var = y_var self.set_ylabels(label_from_attrs(self.data[y_var])) else: self.set_ylabels(y_var) return self def set_xlabels(self, label=None, **kwargs): """Label the x axis on the bottom row of the grid.""" if label is None: label = label_from_attrs(self.data[self._x_var]) for ax in self._bottom_axes: ax.set_xlabel(label, **kwargs) return self def set_ylabels(self, label=None, **kwargs): """Label the y axis on the left column of the grid.""" if label is None: label = label_from_attrs(self.data[self._y_var]) for ax in self._left_axes: ax.set_ylabel(label, **kwargs) return self def set_titles(self, template="{coord} = {value}", maxchar=30, size=None, **kwargs): """ Draw titles either above each facet or on the grid margins. Parameters ---------- template : string Template for plot titles containing {coord} and {value} maxchar : int 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.axes.flat): # 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.axes[:, -1], self.row_names, self.row_labels) ): 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) # The column titles on the top row for index, (ax, col_name, handle) in enumerate( zip(self.axes[0, :], self.col_names, self.col_labels) ): 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) return self def set_ticks(self, max_xticks=_NTICKS, max_yticks=_NTICKS, fontsize=_FONTSIZE): """ 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.axes.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) return self def map(self, func, *args, **kwargs): """ 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 : strings 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 """ plt = import_matplotlib_pyplot() for ax, namedict in zip(self.axes.flat, self.name_dicts.flat): if namedict is not None: data = self.data.loc[namedict] plt.sca(ax) innerargs = [data[a].values 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, plotfunc, kind, x=None, y=None, row=None, col=None, col_wrap=None, sharex=True, sharey=True, aspect=None, size=None, subplot_kws=None, ax=None, figsize=None, **kwargs, ): """ 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") 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 == "dataset": return g.map_dataset(plotfunc, x, y, **kwargs)