def plot_stack(
self: hist.stack.Stack,
*,
ax: matplotlib.axes.Axes | None = None,
legend: bool | None = False,
**kwargs: Any,
) -> Any:
if self[0].ndim != 1:
raise NotImplementedError("Please project to 1D before calling plot")
if "label" not in kwargs:
if all(h.name is not None for h in self):
kwargs["label"] = [h.name for h in self]
ret = histplot(list(self), ax=ax, **kwargs)
final_ax = ret[0].stairs.axes
_plot_keywords_wrapper(final_ax, legend=legend)
return ret
def _plot_ratiolike(
self: hist.BaseHist,
other: Union[hist.BaseHist, Callable[[np.ndarray], np.ndarray], str],
likelihood: bool = False,
*,
ax_dict: Optional[Dict[str, matplotlib.axes.Axes]] = None,
view: Literal["ratio", "pull"],
fit_fmt: Optional[str] = None,
**kwargs: Any,
) -> Tuple[MainAxisArtists, RatiolikeArtists]:
r"""
Plot ratio-like plots (ratio plots and pull plots) for BaseHist
``fit_fmt`` can be a string such as ``r"{name} = {value:.3g} $\pm$ {error:.3g}"``
"""
try:
from iminuit import Minuit # noqa: F401
from scipy.optimize import curve_fit # noqa: F401
except ModuleNotFoundError:
print(
f"Hist.plot_{view} requires scipy and iminuit. Please install hist[plot] or manually install dependencies.",
file=sys.stderr,
)
raise
if self.ndim != 1:
raise TypeError(
f"Only 1D-histogram supports ratio plot, try projecting {self.__class__.__name__} to 1D"
)
if isinstance(other, hist.hist.Hist) and other.ndim != 1:
raise TypeError(
f"Only 1D-histogram supports ratio plot, try projecting other={other.__class__.__name__} to 1D"
)
if ax_dict:
try:
main_ax = ax_dict["main_ax"]
subplot_ax = ax_dict[f"{view}_ax"]
except KeyError:
raise ValueError("All axes should be all given or none at all")
else:
fig = plt.gcf()
grid = fig.add_gridspec(2, 1, hspace=0, height_ratios=[3, 1])
main_ax = fig.add_subplot(grid[0])
subplot_ax = fig.add_subplot(grid[1], sharex=main_ax)
plt.setp(main_ax.get_xticklabels(), visible=False)
# Keyword Argument Conversion: convert the kwargs to several independent args
# error bar keyword arguments
eb_kwargs = _filter_dict(kwargs, "eb_")
eb_kwargs.setdefault("label", "Histogram Data")
# Use "fmt" over "marker" to avoid UserWarning on keyword precedence
eb_kwargs.setdefault("fmt", "o")
eb_kwargs.setdefault("linestyle", "none")
# fit plot keyword arguments
fp_kwargs = _filter_dict(kwargs, "fp_")
fp_kwargs.setdefault("label", "Counts")
# bar plot keyword arguments
bar_kwargs = _filter_dict(kwargs, "bar_", ignore={"bar_width"})
# uncertainty band keyword arguments
ub_kwargs = _filter_dict(kwargs, "ub_")
ub_kwargs.setdefault("label", "Uncertainty")
# ratio plot keyword arguments
rp_kwargs = _filter_dict(kwargs, "rp_")
rp_kwargs.setdefault("uncertainty_type", "poisson")
rp_kwargs.setdefault("legend_loc", "best")
rp_kwargs.setdefault("num_label", None)
rp_kwargs.setdefault("denom_label", None)
# patch plot keyword arguments
pp_kwargs = _filter_dict(kwargs, "pp_")
# Judge whether some arguments are left
if kwargs:
raise ValueError(f"{set(kwargs)}' not needed")
main_ax.set_ylabel(fp_kwargs["label"])
# Computation and Fit
hist_values = self.values()
main_ax_artists: MainAxisArtists # Type now due to control flow
if callable(other) or isinstance(other, str):
if isinstance(other, str):
if other in {"gauss", "gaus", "normal"}:
other = _construct_gaussian_callable(self)
else:
other = _expr_to_lambda(other)
(
compare_values,
model_uncert,
hist_values_uncert,
bestfit_result,
) = _fit_callable_to_hist(other, self, likelihood)
if fit_fmt is not None:
parnames = list(inspect.signature(other).parameters)[1:]
popt, pcov = bestfit_result
perr = np.sqrt(np.diag(pcov))
fp_label = "Fit"
for name, value, error in zip(parnames, popt, perr):
fp_label += "\n "
fp_label += fit_fmt.format(name=name, value=value, error=error)
fp_kwargs["label"] = fp_label
else:
fp_kwargs["label"] = "Fitted value"
main_ax_artists = _plot_fit_result(
self,
model_values=compare_values,
model_uncert=model_uncert,
ax=main_ax,
eb_kwargs=eb_kwargs,
fp_kwargs=fp_kwargs,
ub_kwargs=ub_kwargs,
)
else:
compare_values = other.values()
self_artists = histplot(self, ax=main_ax, label=rp_kwargs["num_label"])
other_artists = histplot(other, ax=main_ax, label=rp_kwargs["denom_label"])
main_ax_artists = self_artists, other_artists
subplot_ax_artists: RatiolikeArtists # Type now due to control flow
# Compute ratios: containing no INF values
with np.errstate(divide="ignore", invalid="ignore"):
if view == "ratio":
ratios = hist_values / compare_values
ratio_uncert = ratio_uncertainty(
num=hist_values,
denom=compare_values,
uncertainty_type=rp_kwargs["uncertainty_type"],
)
# ratio: plot the ratios using Matplotlib errorbar or bar
subplot_ax_artists = plot_ratio_array(
self, ratios, ratio_uncert, ax=subplot_ax, **rp_kwargs
)
elif view == "pull":
pulls = (hist_values - compare_values) / hist_values_uncert
pulls[np.isnan(pulls) | np.isinf(pulls)] = 0
# Pass dicts instead of unpacking to avoid conflicts
subplot_ax_artists = plot_pull_array(
self, pulls, ax=subplot_ax, bar_kwargs=bar_kwargs, pp_kwargs=pp_kwargs
)
if main_ax.get_legend_handles_labels()[0]: # Don't plot an empty legend
main_ax.legend(loc=rp_kwargs["legend_loc"])
return main_ax_artists, subplot_ax_artists
def plot2d_full(
self: hist.BaseHist,
*,
ax_dict: Optional[Dict[str, matplotlib.axes.Axes]] = None,
**kwargs: Any,
) -> Tuple[Hist2DArtists, Hist1DArtists, Hist1DArtists]:
"""
Plot2d_full method for BaseHist object.
Pass a dict of axes to ``ax_dict``, otherwise, the current figure will be used.
"""
# Type judgement
if self.ndim != 2:
raise TypeError("Only 2D-histogram has plot2d_full")
if ax_dict is None:
ax_dict = {}
# Default Figure: construct the figure and axes
if ax_dict:
try:
main_ax = ax_dict["main_ax"]
top_ax = ax_dict["top_ax"]
side_ax = ax_dict["side_ax"]
except KeyError:
raise ValueError("All axes should be all given or none at all")
else:
fig = plt.gcf()
grid = fig.add_gridspec(
2, 2, hspace=0, wspace=0, width_ratios=[4, 1], height_ratios=[1, 4]
)
main_ax = fig.add_subplot(grid[1, 0])
top_ax = fig.add_subplot(grid[0, 0], sharex=main_ax)
side_ax = fig.add_subplot(grid[1, 1], sharey=main_ax)
# keyword arguments
main_kwargs = _filter_dict(kwargs, "main_", ignore={"main_cbar"})
top_kwargs = _filter_dict(kwargs, "top_")
side_kwargs = _filter_dict(kwargs, "side_")
# judge whether some arguments left
if len(kwargs):
raise ValueError(f"{set(kwargs)} not needed")
# Plot: plot the 2d-histogram
# main plot
main_art = hist2dplot(self, ax=main_ax, cbar=False, **main_kwargs)
# top plot
top_art = histplot(
self.project(self.axes[0].name or 0),
ax=top_ax,
**top_kwargs,
)
top_ax.spines["top"].set_visible(False)
top_ax.spines["right"].set_visible(False)
top_ax.xaxis.set_visible(False)
top_ax.set_ylabel("Counts")
# side plot
base = side_ax.transData
rot = transforms.Affine2D().rotate_deg(90).scale(-1, 1)
side_art = histplot(
self.project(self.axes[1].name or 1),
ax=side_ax,
transform=rot + base,
**side_kwargs,
)
side_ax.spines["top"].set_visible(False)
side_ax.spines["right"].set_visible(False)
side_ax.yaxis.set_visible(False)
side_ax.set_xlabel("Counts")
return main_art, top_art, side_art