def __init__(
self,
variable: HistVariable,
) -> None:
self._variable = variable # type: HistVariable
self._histograms = HistogramContainer() # type: HistogramContainer
self._last_figure = None # type: Optional[FigureType]
class HistogramPlot(ABC):
"""
Base class for histogram plots.
"""
legend_cols_default = 1
legend_loc_default = plt.rcParams["legend.loc"]
legend_font_size_default = plt.rcParams["legend.fontsize"]
def __init__(self, variable: HistVariable) -> None:
self._variable = variable # type: HistVariable
self._histograms = HistogramContainer()
self._last_figure = None # type: Optional[FigureType]
@abstractmethod
def plot_on(
self, *args, **kwargs
) -> Union[AxesType, Tuple[FigureType, Tuple[AxesType, AxesType]], Any]:
raise NotImplementedError(
f"The 'plot_on' method is not implemented for the class {self.__class__.__name__}!"
)
@abstractmethod
def add_component(self, *args, **kwargs) -> None:
raise NotImplementedError(
f"The 'add_component' method is not implemented for the class {self.__class__.__name__}!"
)
def _add_component(self,
label: str,
histogram_key: str,
data: DataInputType,
weights: WeightsInputType = None,
systematics: SystematicsInputType = None,
hist_type: Optional[str] = None,
color: Optional[str] = None,
alpha: float = 1.0) -> None:
if histogram_key not in self._histograms.histogram_keys:
new_histogram = Histogram(variable=self.variable,
hist_type=hist_type)
self._histograms.add_histogram(key=histogram_key,
histogram=new_histogram)
self._histograms[histogram_key].add_histogram_component(
label=label,
data=data,
weights=weights,
systematics=systematics,
data_column_names=self.variable.df_label,
color=color,
alpha=alpha)
def _add_prebinned_component(
self,
label: str,
histogram_key: str,
bin_counts: np.ndarray,
original_binning: Binning,
bin_errors_squared: np.ndarray = None,
data_column_names: DataColumnNamesInput = None,
hist_type: Optional[str] = None,
color: Optional[str] = None,
alpha: float = 1.0) -> None:
if histogram_key not in self._histograms.histogram_keys:
new_histogram = Histogram(variable=self.variable,
hist_type=hist_type)
self._histograms.add_histogram(key=histogram_key,
histogram=new_histogram)
if data_column_names is not None:
if isinstance(data_column_names, str):
assert data_column_names == self.variable.df_label, (
data_column_names, self.variable.df_label)
elif isinstance(data_column_names, list):
assert len(data_column_names) == 1, (len(data_column_names),
data_column_names)
assert data_column_names[0] == self.variable.df_label, (
data_column_names[0], self.variable.df_label)
else:
raise RuntimeError(
f"Unexpected type for argument 'data_column_names':\n"
f"Should be None, str or List[str], but is {type(data_column_names)}!"
)
self._histograms[histogram_key].add_histogram_component(
label=label,
bin_counts=bin_counts,
original_binning=original_binning,
bin_errors_squared=bin_errors_squared,
data_column_names=self.variable.df_label,
color=color,
alpha=alpha)
@property
def binning(self) -> Optional[Binning]:
return self._histograms.common_binning
@property
def bin_edges(self) -> Tuple[float, ...]:
assert len(self.binning.bin_edges) == 1, self.binning.bin_edges
return self.binning.bin_edges[0]
@property
def bin_widths(self) -> np.ndarray:
assert len(self.binning.bin_widths) == 1, self.binning.bin_widths
return np.array(self.binning.bin_widths[0])
@property
def bin_mids(self) -> Tuple[float, ...]:
assert len(self.binning.bin_mids) == 1, self.binning.bin_mids
return self.binning.bin_mids[0]
@property
def number_of_bins(self) -> int:
assert len(self.binning.num_bins) == 1, self.binning.num_bins
return self.binning.num_bins[0]
@property
def minimal_bin_width(self) -> float:
return min(self.bin_widths)
@property
def variable(self) -> HistVariable:
return self._variable
@property
def number_of_histograms(self) -> int:
return self._histograms.number_of_histograms
def reset_binning_to_use_raw_data_range(self) -> None:
self._histograms.reset_binning_to_use_raw_data_range_of_all()
def reset_binning_to_use_raw_data_range_of_histogram(
self, histogram_key: str) -> None:
self._histograms.reset_binning_to_use_raw_data_range_of_key(
key=histogram_key)
def apply_adaptive_binning_based_on_histogram(
self,
histogram_key: str,
minimal_bin_count: int = 5,
minimal_number_of_bins: int = 7) -> None:
self._histograms.apply_adaptive_binning_based_on_key(
key=histogram_key,
minimal_bin_count=minimal_bin_count,
minimal_number_of_bins=minimal_number_of_bins)
def _get_y_label(self, normed: bool, evts_or_cands: str = "Events") -> str:
if normed:
return "Normalized in arb. units"
elif self._variable.use_log_scale:
return f"{evts_or_cands} / Bin"
else:
return "{e} / {bo}{b:.2g}{v}{bc}".format(
e=evts_or_cands,
b=self.minimal_bin_width,
v=" " + self._variable.unit if self._variable.unit else "",
bo="(" if self._variable.unit else "",
bc=")" if self._variable.unit else "")
def draw_legend(self,
axis: AxesType,
inside: bool,
loc: Optional[Union[int, str]] = None,
ncols: Optional[int] = None,
y_axis_scale: Optional[float] = None,
font_size: Optional[Union[int, float, str]] = None,
bbox_to_anchor_tuple: Tuple[float, float] = None) -> None:
if loc is None:
loc = self.legend_loc_default
if ncols is None:
ncols = self.legend_cols_default
if font_size is None:
font_size = self.legend_font_size_default
if inside:
axis.legend(frameon=False, loc=loc, ncol=ncols, fontsize=font_size)
if y_axis_scale is not None:
y_limits = axis.get_ylim()
axis.set_ylim(bottom=y_limits[0],
top=y_axis_scale * y_limits[1])
else:
if bbox_to_anchor_tuple is None:
bbox_to_anchor_tuple = (1., 1.)
axis.legend(frameon=False,
loc=loc,
ncol=ncols,
bbox_to_anchor=bbox_to_anchor_tuple)
def get_last_figure(self) -> Optional[FigureType]:
return self._last_figure
def write_hist_data_to_file(self, file_path: Union[os.PathLike, AnyStr]):
self._histograms.write_to_file(file_path=file_path)