def test_repr(self):
assert str(Mappable(.5)) == "<0.5>"
assert str(Mappable("CO")) == "<'CO'>"
assert str(Mappable(rc="lines.linewidth")) == "<rc:lines.linewidth>"
assert str(Mappable(depend="color")) == "<depend:color>"
assert str(Mappable(auto=True)) == "<auto>"
def test_depends(self):
val = 2
df = pd.DataFrame(index=pd.RangeIndex(10))
m = self.mark(pointsize=Mappable(val), linewidth=Mappable(depend="pointsize"))
assert m._resolve({}, "linewidth") == val
assert_array_equal(m._resolve(df, "linewidth"), np.full(len(df), val))
m = self.mark(pointsize=val * 2, linewidth=Mappable(depend="pointsize"))
assert m._resolve({}, "linewidth") == val * 2
assert_array_equal(m._resolve(df, "linewidth"), np.full(len(df), val * 2))
class Bar(BarBase):
"""
An rectangular mark drawn between baseline and data values.
"""
color: MappableColor = Mappable("C0", grouping=False)
alpha: MappableFloat = Mappable(.7, grouping=False)
fill: MappableBool = Mappable(True, grouping=False)
edgecolor: MappableColor = Mappable(depend="color", grouping=False)
edgealpha: MappableFloat = Mappable(1, grouping=False)
edgewidth: MappableFloat = Mappable(rc="patch.linewidth", grouping=False)
edgestyle: MappableStyle = Mappable("-", grouping=False)
# pattern: MappableString = Mappable(None) # TODO no Property yet
width: MappableFloat = Mappable(.8, grouping=False)
baseline: MappableFloat = Mappable(0, grouping=False) # TODO *is* this mappable?
def _plot(self, split_gen, scales, orient):
val_idx = ["y", "x"].index(orient)
for _, data, ax in split_gen():
bars, vals = self._make_patches(data, scales, orient)
for bar in bars:
# Because we are clipping the artist (see below), the edges end up
# looking half as wide as they actually are. I don't love this clumsy
# workaround, which is going to cause surprises if you work with the
# artists directly. We may need to revisit after feedback.
bar.set_linewidth(bar.get_linewidth() * 2)
linestyle = bar.get_linestyle()
if linestyle[1]:
linestyle = (linestyle[0], tuple(x / 2 for x in linestyle[1]))
bar.set_linestyle(linestyle)
# This is a bit of a hack to handle the fact that the edge lines are
# centered on the actual extents of the bar, and overlap when bars are
# stacked or dodged. We may discover that this causes problems and needs
# to be revisited at some point. Also it should be faster to clip with
# a bbox than a path, but I cant't work out how to get the intersection
# with the axes bbox.
bar.set_clip_path(bar.get_path(), bar.get_transform() + ax.transData)
if self.artist_kws.get("clip_on", True):
# It seems the above hack undoes the default axes clipping
bar.set_clip_box(ax.bbox)
bar.sticky_edges[val_idx][:] = (0, np.inf)
ax.add_patch(bar)
# Add a container which is useful for, e.g. Axes.bar_label
if Version(mpl.__version__) >= Version("3.4.0"):
orientation = {"x": "vertical", "y": "horizontal"}[orient]
container_kws = dict(datavalues=vals, orientation=orientation)
else:
container_kws = {}
container = mpl.container.BarContainer(bars, **container_kws)
ax.add_container(container)
class Line(Mark):
"""
A mark connecting data points with sorting along the orientation axis.
"""
# TODO other semantics (marker?)
color: MappableColor = Mappable("C0", )
alpha: MappableFloat = Mappable(1, )
linewidth: MappableFloat = Mappable(rc="lines.linewidth", )
linestyle: MappableString = Mappable(rc="lines.linestyle", )
# TODO alternately, have Path mark that doesn't sort
sort: bool = True
def _plot(self, split_gen, scales, orient):
for keys, data, ax in split_gen(dropna=False):
keys = resolve_properties(self, keys, scales)
if self.sort:
# TODO where to dropna?
data = data.sort_values(orient)
else:
data.loc[data.isna().any(axis=1), ["x", "y"]] = np.nan
line = mpl.lines.Line2D(
data["x"].to_numpy(),
data["y"].to_numpy(),
color=keys["color"],
alpha=keys["alpha"],
linewidth=keys["linewidth"],
linestyle=keys["linestyle"],
**self.
artist_kws, # TODO keep? remove? be consistent across marks
)
ax.add_line(line)
def _legend_artist(self, variables, value, scales):
key = resolve_properties(self, {v: value for v in variables}, scales)
return mpl.lines.Line2D(
[],
[],
color=key["color"],
alpha=key["alpha"],
linewidth=key["linewidth"],
linestyle=key["linestyle"],
)
def test_default(self):
val = 3
m = self.mark(linewidth=Mappable(val))
assert m._resolve({}, "linewidth") == val
df = pd.DataFrame(index=pd.RangeIndex(10))
assert_array_equal(m._resolve(df, "linewidth"), np.full(len(df), val))
def test_fillcolor(self):
c, a = "green", .8
fa = .2
m = self.mark(
color=c, alpha=a,
fillcolor=Mappable(depend="color"), fillalpha=Mappable(fa),
)
assert resolve_color(m, {}) == mpl.colors.to_rgba(c, a)
assert resolve_color(m, {}, "fill") == mpl.colors.to_rgba(c, fa)
df = pd.DataFrame(index=pd.RangeIndex(10))
cs = [c] * len(df)
assert_array_equal(resolve_color(m, df), mpl.colors.to_rgba_array(cs, a))
assert_array_equal(
resolve_color(m, df, "fill"), mpl.colors.to_rgba_array(cs, fa)
)
def test_rcparam(self):
param = "lines.linewidth"
val = mpl.rcParams[param]
m = self.mark(linewidth=Mappable(rc=param))
assert m._resolve({}, "linewidth") == val
df = pd.DataFrame(index=pd.RangeIndex(10))
assert_array_equal(m._resolve(df, "linewidth"), np.full(len(df), val))
class MockMark(Mark):
linewidth: float = Mappable(rc="lines.linewidth")
pointsize: float = Mappable(4)
color: str = Mappable("C0")
fillcolor: str = Mappable(depend="color")
alpha: float = Mappable(1)
fillalpha: float = Mappable(depend="alpha")
def test_mapped(self):
values = {"a": 1, "b": 2, "c": 3}
def f(x):
return np.array([values[x_i] for x_i in x])
m = self.mark(linewidth=Mappable(2))
scales = {"linewidth": f}
assert m._resolve({"linewidth": "c"}, "linewidth", scales) == 3
df = pd.DataFrame({"linewidth": ["a", "b", "c"]})
expected = np.array([1, 2, 3], float)
assert_array_equal(m._resolve(df, "linewidth", scales), expected)
class Dot(Scatter):
"""
A point mark defined by shape with optional edges.
"""
marker: MappableString = Mappable("o", grouping=False)
color: MappableColor = Mappable("C0", grouping=False)
alpha: MappableFloat = Mappable(1, grouping=False)
fill: MappableBool = Mappable(True, grouping=False)
edgecolor: MappableColor = Mappable(depend="color", grouping=False)
edgealpha: MappableFloat = Mappable(depend="alpha", grouping=False)
pointsize: MappableFloat = Mappable(6, grouping=False) # TODO rcParam?
edgewidth: MappableFloat = Mappable(.5, grouping=False) # TODO rcParam?
edgestyle: MappableStyle = Mappable("-", grouping=False)
def _resolve_properties(self, data, scales):
# TODO this is maybe a little hacky, is there a better abstraction?
resolved = super()._resolve_properties(data, scales)
filled = resolved["fill"]
main_stroke = resolved["stroke"]
edge_stroke = resolved["edgewidth"]
resolved["linewidth"] = np.where(filled, edge_stroke, main_stroke)
# Overwrite the colors that the super class set
main_color = resolve_color(self, data, "", scales)
edge_color = resolve_color(self, data, "edge", scales)
if not np.isscalar(filled):
# Expand dims to use in np.where with rgba arrays
filled = filled[:, None]
resolved["edgecolor"] = np.where(filled, edge_color, main_color)
filled = np.squeeze(filled)
if isinstance(main_color, tuple):
main_color = tuple([*main_color[:3], main_color[3] * filled])
else:
main_color = np.c_[main_color[:, :3], main_color[:, 3] * filled]
resolved["facecolor"] = main_color
return resolved
class Area(AreaBase, Mark):
"""
An interval mark that fills between baseline and data values.
"""
color: MappableColor = Mappable("C0", )
alpha: MappableFloat = Mappable(.2, )
fill: MappableBool = Mappable(True, )
edgecolor: MappableColor = Mappable(depend="color")
edgealpha: MappableFloat = Mappable(1, )
edgewidth: MappableFloat = Mappable(rc="patch.linewidth", )
edgestyle: MappableStyle = Mappable("-", )
# TODO should this be settable / mappable?
baseline: MappableFloat = Mappable(0, grouping=False)
def _standardize_coordinate_parameters(self, data, orient):
dv = {"x": "y", "y": "x"}[orient]
return data.rename(columns={"baseline": f"{dv}min", dv: f"{dv}max"})
def test_color_mapped_alpha(self):
c = "r"
values = {"a": .2, "b": .5, "c": .8}
m = self.mark(color=c, alpha=Mappable(1))
scales = {"alpha": lambda s: np.array([values[s_i] for s_i in s])}
assert resolve_color(m, {"alpha": "b"}, "", scales) == mpl.colors.to_rgba(c, .5)
df = pd.DataFrame({"alpha": list(values.keys())})
# Do this in two steps for mpl 3.2 compat
expected = mpl.colors.to_rgba_array([c] * len(df))
expected[:, 3] = list(values.values())
assert_array_equal(resolve_color(m, df, "", scales), expected)
class Ribbon(AreaBase, Mark):
"""
An interval mark that fills between minimum and maximum values.
"""
color: MappableColor = Mappable("C0", )
alpha: MappableFloat = Mappable(.2, )
fill: MappableBool = Mappable(True, )
edgecolor: MappableColor = Mappable(depend="color", )
edgealpha: MappableFloat = Mappable(1, )
edgewidth: MappableFloat = Mappable(0, )
edgestyle: MappableFloat = Mappable("-", )
def _standardize_coordinate_parameters(self, data, orient):
# dv = {"x": "y", "y": "x"}[orient]
# TODO assert that all(ymax >= ymin)?
return data
class Area(AreaBase, Mark):
"""
An interval mark that fills between baseline and data values.
"""
color: MappableColor = Mappable("C0", )
alpha: MappableFloat = Mappable(.2, )
fill: MappableBool = Mappable(True, )
edgecolor: MappableColor = Mappable(depend="color")
edgealpha: MappableFloat = Mappable(1, )
edgewidth: MappableFloat = Mappable(rc="patch.linewidth", )
edgestyle: MappableStyle = Mappable("-", )
# TODO should this be settable / mappable?
baseline: MappableFloat = Mappable(0, grouping=False)
def _standardize_coordinate_parameters(self, data, orient):
dv = {"x": "y", "y": "x"}[orient]
return data.rename(columns={"baseline": f"{dv}min", dv: f"{dv}max"})
def _postprocess_artist(self, artist, ax, orient):
# TODO copying a lot of code from Bar, let's abstract this
# See comments there, I am not going to repeat them too
artist.set_linewidth(artist.get_linewidth() * 2)
linestyle = artist.get_linestyle()
if linestyle[1]:
linestyle = (linestyle[0], tuple(x / 2 for x in linestyle[1]))
artist.set_linestyle(linestyle)
artist.set_clip_path(artist.get_path(),
artist.get_transform() + ax.transData)
if self.artist_kws.get("clip_on", True):
artist.set_clip_box(ax.bbox)
val_idx = ["y", "x"].index(orient)
artist.sticky_edges[val_idx][:] = (0, np.inf)
class Path(Mark):
"""
A mark connecting data points in the order they appear.
"""
color: MappableColor = Mappable("C0")
alpha: MappableFloat = Mappable(1)
linewidth: MappableFloat = Mappable(rc="lines.linewidth")
linestyle: MappableString = Mappable(rc="lines.linestyle")
marker: MappableString = Mappable(rc="lines.marker")
pointsize: MappableFloat = Mappable(rc="lines.markersize")
fillcolor: MappableColor = Mappable(depend="color")
edgecolor: MappableColor = Mappable(depend="color")
edgewidth: MappableFloat = Mappable(rc="lines.markeredgewidth")
_sort: ClassVar[bool] = False
def _plot(self, split_gen, scales, orient):
for keys, data, ax in split_gen(keep_na=not self._sort):
vals = resolve_properties(self, keys, scales)
vals["color"] = resolve_color(self, keys, scales=scales)
vals["fillcolor"] = resolve_color(self, keys, prefix="fill", scales=scales)
vals["edgecolor"] = resolve_color(self, keys, prefix="edge", scales=scales)
# https://github.com/matplotlib/matplotlib/pull/16692
if Version(mpl.__version__) < Version("3.3.0"):
vals["marker"] = vals["marker"]._marker
if self._sort:
data = data.sort_values(orient)
artist_kws = self.artist_kws.copy()
self._handle_capstyle(artist_kws, vals)
line = mpl.lines.Line2D(
data["x"].to_numpy(),
data["y"].to_numpy(),
color=vals["color"],
linewidth=vals["linewidth"],
linestyle=vals["linestyle"],
marker=vals["marker"],
markersize=vals["pointsize"],
markerfacecolor=vals["fillcolor"],
markeredgecolor=vals["edgecolor"],
markeredgewidth=vals["edgewidth"],
**artist_kws,
)
ax.add_line(line)
def _legend_artist(self, variables, value, scales):
keys = {v: value for v in variables}
vals = resolve_properties(self, keys, scales)
vals["color"] = resolve_color(self, keys, scales=scales)
vals["fillcolor"] = resolve_color(self, keys, prefix="fill", scales=scales)
vals["edgecolor"] = resolve_color(self, keys, prefix="edge", scales=scales)
# https://github.com/matplotlib/matplotlib/pull/16692
if Version(mpl.__version__) < Version("3.3.0"):
vals["marker"] = vals["marker"]._marker
artist_kws = self.artist_kws.copy()
self._handle_capstyle(artist_kws, vals)
return mpl.lines.Line2D(
[], [],
color=vals["color"],
linewidth=vals["linewidth"],
linestyle=vals["linestyle"],
marker=vals["marker"],
markersize=vals["pointsize"],
markerfacecolor=vals["fillcolor"],
markeredgecolor=vals["edgecolor"],
markeredgewidth=vals["edgewidth"],
**artist_kws,
)
def _handle_capstyle(self, kws, vals):
# Work around for this matplotlib issue:
# https://github.com/matplotlib/matplotlib/issues/23437
if vals["linestyle"][1] is None:
capstyle = kws.get("solid_capstyle", mpl.rcParams["lines.solid_capstyle"])
kws["dash_capstyle"] = capstyle
class Paths(Mark):
"""
A faster but less-flexible mark for drawing many paths.
"""
color: MappableColor = Mappable("C0")
alpha: MappableFloat = Mappable(1)
linewidth: MappableFloat = Mappable(rc="lines.linewidth")
linestyle: MappableString = Mappable(rc="lines.linestyle")
_sort: ClassVar[bool] = False
def __post_init__(self):
# LineCollection artists have a capstyle property but don't source its value
# from the rc, so we do that manually here. Unfortunately, because we add
# only one LineCollection, we have the use the same capstyle for all lines
# even when they are dashed. It's a slight inconsistency, but looks fine IMO.
self.artist_kws.setdefault("capstyle", mpl.rcParams["lines.solid_capstyle"])
def _setup_lines(self, split_gen, scales, orient):
line_data = {}
for keys, data, ax in split_gen(keep_na=not self._sort):
if ax not in line_data:
line_data[ax] = {
"segments": [],
"colors": [],
"linewidths": [],
"linestyles": [],
}
vals = resolve_properties(self, keys, scales)
vals["color"] = resolve_color(self, keys, scales=scales)
if self._sort:
data = data.sort_values(orient)
# Column stack to avoid block consolidation
xy = np.column_stack([data["x"], data["y"]])
line_data[ax]["segments"].append(xy)
line_data[ax]["colors"].append(vals["color"])
line_data[ax]["linewidths"].append(vals["linewidth"])
line_data[ax]["linestyles"].append(vals["linestyle"])
return line_data
def _plot(self, split_gen, scales, orient):
line_data = self._setup_lines(split_gen, scales, orient)
for ax, ax_data in line_data.items():
lines = mpl.collections.LineCollection(**ax_data, **self.artist_kws)
# Handle datalim update manually
# https://github.com/matplotlib/matplotlib/issues/23129
ax.add_collection(lines, autolim=False)
xy = np.concatenate(ax_data["segments"])
ax.update_datalim(xy)
def _legend_artist(self, variables, value, scales):
key = resolve_properties(self, {v: value for v in variables}, scales)
artist_kws = self.artist_kws.copy()
capstyle = artist_kws.pop("capstyle")
artist_kws["solid_capstyle"] = capstyle
artist_kws["dash_capstyle"] = capstyle
return mpl.lines.Line2D(
[], [],
color=key["color"],
linewidth=key["linewidth"],
linestyle=key["linestyle"],
**artist_kws,
)
class Scatter(Mark):
"""
A point mark defined by strokes with optional fills.
"""
# TODO retype marker as MappableMarker
marker: MappableString = Mappable(rc="scatter.marker", grouping=False)
stroke: MappableFloat = Mappable(.75, grouping=False) # TODO rcParam?
pointsize: MappableFloat = Mappable(3, grouping=False) # TODO rcParam?
color: MappableColor = Mappable("C0", grouping=False)
alpha: MappableFloat = Mappable(1, grouping=False) # TODO auto alpha?
fill: MappableBool = Mappable(True, grouping=False)
fillcolor: MappableColor = Mappable(depend="color", grouping=False)
fillalpha: MappableFloat = Mappable(.2, grouping=False)
def _resolve_paths(self, data):
paths = []
path_cache = {}
marker = data["marker"]
def get_transformed_path(m):
return m.get_path().transformed(m.get_transform())
if isinstance(marker, mpl.markers.MarkerStyle):
return get_transformed_path(marker)
for m in marker:
if m not in path_cache:
path_cache[m] = get_transformed_path(m)
paths.append(path_cache[m])
return paths
def _resolve_properties(self, data, scales):
resolved = resolve_properties(self, data, scales)
resolved["path"] = self._resolve_paths(resolved)
if isinstance(data, dict): # TODO need a better way to check
filled_marker = resolved["marker"].is_filled()
else:
filled_marker = [m.is_filled() for m in resolved["marker"]]
resolved["linewidth"] = resolved["stroke"]
resolved["fill"] = resolved["fill"] * filled_marker
resolved["size"] = resolved["pointsize"]**2
resolved["edgecolor"] = resolve_color(self, data, "", scales)
resolved["facecolor"] = resolve_color(self, data, "fill", scales)
# Because only Dot, and not Scatter, has an edgestyle
resolved.setdefault("edgestyle", (0, None))
fc = resolved["facecolor"]
if isinstance(fc, tuple):
resolved["facecolor"] = fc[0], fc[1], fc[
2], fc[3] * resolved["fill"]
else:
fc[:,
3] = fc[:,
3] * resolved["fill"] # TODO Is inplace mod a problem?
resolved["facecolor"] = fc
return resolved
def _plot(self, split_gen, scales, orient):
# TODO Not backcompat with allowed (but nonfunctional) univariate plots
# (That should be solved upstream by defaulting to "" for unset x/y?)
# (Be mindful of xmin/xmax, etc!)
for keys, data, ax in split_gen():
offsets = np.column_stack([data["x"], data["y"]])
data = self._resolve_properties(data, scales)
points = mpl.collections.PathCollection(
offsets=offsets,
paths=data["path"],
sizes=data["size"],
facecolors=data["facecolor"],
edgecolors=data["edgecolor"],
linewidths=data["linewidth"],
linestyles=data["edgestyle"],
transOffset=ax.transData,
transform=mpl.transforms.IdentityTransform(),
)
ax.add_collection(points)
def _legend_artist(
self,
variables: list[str],
value: Any,
scales: dict[str, Scale],
) -> Artist:
key = {v: value for v in variables}
res = self._resolve_properties(key, scales)
return mpl.collections.PathCollection(
paths=[res["path"]],
sizes=[res["size"]],
facecolors=[res["facecolor"]],
edgecolors=[res["edgecolor"]],
linewidths=[res["linewidth"]],
linestyles=[res["edgestyle"]],
transform=mpl.transforms.IdentityTransform(),
)
class Bars(BarBase):
"""
A faster Bar mark with defaults that are more suitable for histograms.
"""
color: MappableColor = Mappable("C0", grouping=False)
alpha: MappableFloat = Mappable(.7, grouping=False)
fill: MappableBool = Mappable(True, grouping=False)
edgecolor: MappableColor = Mappable(rc="patch.edgecolor", grouping=False)
edgealpha: MappableFloat = Mappable(1, grouping=False)
edgewidth: MappableFloat = Mappable(auto=True, grouping=False)
edgestyle: MappableStyle = Mappable("-", grouping=False)
# pattern: MappableString = Mappable(None) # TODO no Property yet
width: MappableFloat = Mappable(1, grouping=False)
baseline: MappableFloat = Mappable(0, grouping=False) # TODO *is* this mappable?
def _plot(self, split_gen, scales, orient):
ori_idx = ["x", "y"].index(orient)
val_idx = ["y", "x"].index(orient)
patches = defaultdict(list)
for _, data, ax in split_gen():
bars, _ = self._make_patches(data, scales, orient)
patches[ax].extend(bars)
collections = {}
for ax, ax_patches in patches.items():
col = mpl.collections.PatchCollection(ax_patches, match_original=True)
col.sticky_edges[val_idx][:] = (0, np.inf)
ax.add_collection(col, autolim=False)
collections[ax] = col
# Workaround for matplotlib autoscaling bug
# https://github.com/matplotlib/matplotlib/issues/11898
# https://github.com/matplotlib/matplotlib/issues/23129
xys = np.vstack([path.vertices for path in col.get_paths()])
ax.update_datalim(xys)
if "edgewidth" not in scales and isinstance(self.edgewidth, Mappable):
for ax in collections:
ax.autoscale_view()
def get_dimensions(collection):
edges, widths = [], []
for verts in (path.vertices for path in collection.get_paths()):
edges.append(min(verts[:, ori_idx]))
widths.append(np.ptp(verts[:, ori_idx]))
return np.array(edges), np.array(widths)
min_width = np.inf
for ax, col in collections.items():
edges, widths = get_dimensions(col)
points = 72 / ax.figure.dpi * abs(
ax.transData.transform([edges + widths] * 2)
- ax.transData.transform([edges] * 2)
)
min_width = min(min_width, min(points[:, ori_idx]))
linewidth = min(.1 * min_width, mpl.rcParams["patch.linewidth"])
for _, col in collections.items():
col.set_linewidth(linewidth)
class Bar(Mark):
"""
An interval mark drawn between baseline and data values with a width.
"""
color: MappableColor = Mappable("C0", )
alpha: MappableFloat = Mappable(.7, )
fill: MappableBool = Mappable(True, )
edgecolor: MappableColor = Mappable(depend="color", )
edgealpha: MappableFloat = Mappable(1, )
edgewidth: MappableFloat = Mappable(rc="patch.linewidth")
edgestyle: MappableStyle = Mappable("-", )
# pattern: MappableString = Mappable(None, ) # TODO no Property yet
width: MappableFloat = Mappable(.8, grouping=False)
baseline: MappableFloat = Mappable(
0, grouping=False) # TODO *is* this mappable?
def _resolve_properties(self, data, scales):
resolved = resolve_properties(self, data, scales)
resolved["facecolor"] = resolve_color(self, data, "", scales)
resolved["edgecolor"] = resolve_color(self, data, "edge", scales)
fc = resolved["facecolor"]
if isinstance(fc, tuple):
resolved["facecolor"] = fc[0], fc[1], fc[
2], fc[3] * resolved["fill"]
else:
fc[:,
3] = fc[:,
3] * resolved["fill"] # TODO Is inplace mod a problem?
resolved["facecolor"] = fc
return resolved
def _plot(self, split_gen, scales, orient):
def coords_to_geometry(x, y, w, b):
# TODO possible too slow with lots of bars (e.g. dense hist)
# Why not just use BarCollection?
if orient == "x":
w, h = w, y - b
xy = x - w / 2, b
else:
w, h = x - b, w
xy = b, y - h / 2
return xy, w, h
for _, data, ax in split_gen():
xys = data[["x", "y"]].to_numpy()
data = self._resolve_properties(data, scales)
bars = []
for i, (x, y) in enumerate(xys):
baseline = data["baseline"][i]
width = data["width"][i]
xy, w, h = coords_to_geometry(x, y, width, baseline)
bar = mpl.patches.Rectangle(
xy=xy,
width=w,
height=h,
facecolor=data["facecolor"][i],
edgecolor=data["edgecolor"][i],
linewidth=data["edgewidth"][i],
linestyle=data["edgestyle"][i],
)
ax.add_patch(bar)
bars.append(bar)
# TODO add container object to ax, line ax.bar does
def _legend_artist(
self,
variables: list[str],
value: Any,
scales: dict[str, Scale],
) -> Artist:
# TODO return some sensible default?
key = {v: value for v in variables}
key = self._resolve_properties(key, scales)
artist = mpl.patches.Patch(
facecolor=key["facecolor"],
edgecolor=key["edgecolor"],
linewidth=key["edgewidth"],
linestyle=key["edgestyle"],
)
return artist
class Paths(Mark):
"""
A faster but less-flexible mark for drawing many paths.
"""
color: MappableColor = Mappable("C0")
alpha: MappableFloat = Mappable(1)
linewidth: MappableFloat = Mappable(rc="lines.linewidth")
linestyle: MappableString = Mappable(rc="lines.linestyle")
_sort: ClassVar[bool] = False
def _plot(self, split_gen, scales, orient):
line_data = {}
for keys, data, ax in split_gen(keep_na=not self._sort):
if ax not in line_data:
line_data[ax] = {
"segments": [],
"colors": [],
"linewidths": [],
"linestyles": [],
}
vals = resolve_properties(self, keys, scales)
vals["color"] = resolve_color(self, keys, scales=scales)
if self._sort:
data = data.sort_values(orient)
# TODO comment about block consolidation
xy = np.column_stack([data["x"], data["y"]])
line_data[ax]["segments"].append(xy)
line_data[ax]["colors"].append(vals["color"])
line_data[ax]["linewidths"].append(vals["linewidth"])
line_data[ax]["linestyles"].append(vals["linestyle"])
for ax, ax_data in line_data.items():
lines = mpl.collections.LineCollection(
**ax_data,
**self.artist_kws,
)
ax.add_collection(lines, autolim=False)
# https://github.com/matplotlib/matplotlib/issues/23129
# TODO get paths from lines object?
xy = np.concatenate(ax_data["segments"])
ax.dataLim.update_from_data_xy(xy,
ax.ignore_existing_data_limits,
updatex=True,
updatey=True)
def _legend_artist(self, variables, value, scales):
key = resolve_properties(self, {v: value for v in variables}, scales)
return mpl.lines.Line2D(
[],
[],
color=key["color"],
linewidth=key["linewidth"],
linestyle=key["linestyle"],
**self.artist_kws,
)
def test_input_checks(self):
with pytest.raises(AssertionError):
Mappable(rc="bogus.parameter")
with pytest.raises(AssertionError):
Mappable(depend="nonexistent_feature")
