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Commit d4f3cf9f authored by Felix Metzner's avatar Felix Metzner
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Minor style changes to shape sys evaluation plot.

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rdstar/offline_analysis/fitting/dedicated_fit_approach/plotting_tools.py
+ 35
− 21
View file @ d4f3cf9f
......@@ -21,8 +21,15 @@ from rdstar.utilities.logging import log_to_default
from rdstar.offline_analysis.fitting.dedicated_fit_approach.dedicated_sys_handler import SystematicsInfo
from rdstar.offline_analysis.fitting.dedicated_fit_approach.fit_result_container import RDStarFitResultContainer
from rdstar.offline_analysis.fitting.dedicated_fit_approach.fit_setups.fit_setup_utilities import FitSetupTriplet
from rdstar.offline_analysis.fitting.dedicated_fit_approach.systematics_manager.shape_sys_evaluater import FitSetupBinningPerRecoChInfo
from rdstar.offline_analysis.fitting.dedicated_fit_approach.fit_info_container import ComponentInfo, FitSetupInfoContainer, FitObservableInfo, RecoChannelInfo
from rdstar.offline_analysis.fitting.dedicated_fit_approach.systematics_manager.shape_sys_evaluater import (
FitSetupBinningPerRecoChInfo,
)
from rdstar.offline_analysis.fitting.dedicated_fit_approach.fit_info_container import (
ComponentInfo,
FitSetupInfoContainer,
FitObservableInfo,
RecoChannelInfo,
)
__all__ = [
......@@ -157,6 +164,7 @@ class NuisancePullPlotter:
# region systematics shape plotter
@dataclass(frozen=True)
class ShapePlotInfoContainer:
name: str
......@@ -188,12 +196,11 @@ class ShapePlotInfoContainer:
@property
def relative_stat_error(self) -> np.ndarray:
norm: np.ndarray = np.where(
self.normed_base_shape > 0,
self.normed_base_shape,
np.ones_like(self.normed_base_shape),
)
# norm: np.ndarray = np.where(
# self.normed_base_shape > 0,
# self.normed_base_shape,
# np.ones_like(self.normed_base_shape),
# )
return np.sqrt(self.pure_bin_counts)
......@@ -312,7 +319,8 @@ class SystematicsShapePlotter:
(
(rel_err * _2d_normed_base_shape).sum(axis=1),
(rel_err * _2d_normed_base_shape).sum(axis=0),
) for rel_err in _2d_relative_shape_error_splits
)
for rel_err in _2d_relative_shape_error_splits
)
sum_of_2d_relative_shape_error_splits: np.ndarray = np.sum(_2d_relative_shape_error_splits, axis=0)
......@@ -440,21 +448,23 @@ class SystematicsShapePlotter:
final_target_dir_path: PathType = os.path.join(target_dir_path, spec_sys_shape_info.observable.col_name)
os.makedirs(final_target_dir_path, exist_ok=True)
fn_subset_str: str = "All" if spec_sys_shape_info.subset_index == -1 else f"SubSet{spec_sys_shape_info.subset_index}"
fn_subset_str: str = (
"All" if spec_sys_shape_info.subset_index == -1 else f"SubSet{spec_sys_shape_info.subset_index}"
)
plot_file_name: str = f"{self.plot_name_prefix}"
plot_file_name += f"_{spec_sys_shape_info.observable.col_name}"
plot_file_name += f"_comp{spec_sys_shape_info.component_info.name}_ch{spec_sys_shape_info.reco_ch_info.name}"
plot_file_name += f"_{spec_sys_shape_info.name}_{fn_subset_str}"
fig, axes = plt.subplots( # type: FigureType, Sequence[AxesType]
fig, axes = plt.subplots(
nrows=2,
ncols=1,
figsize=self.fig_size,
dpi=300,
sharex="all",
gridspec_kw={"height_ratios": [self.height_ratio[0], self.height_ratio[1]]},
)
) # type: FigureType, Sequence[AxesType]
assert len(axes) == 2, len(axes)
ax1: AxesType = axes[0]
......@@ -476,16 +486,18 @@ class SystematicsShapePlotter:
norm_bin_count: np.ndarray = spec_sys_shape_info.normed_base_shape
u_bin_shape_uncert: np.ndarray = spec_sys_shape_info.normed_base_shape * spec_sys_shape_info.relative_shape_error
d_bin_shape_uncert: np.ndarray = spec_sys_shape_info.normed_base_shape * (-1.0 * spec_sys_shape_info.relative_shape_error)
d_bin_shape_uncert: np.ndarray = spec_sys_shape_info.normed_base_shape * (
-1.0 * spec_sys_shape_info.relative_shape_error
)
bin_stat_uncert: np.ndarray = spec_sys_shape_info.relative_stat_error
assert np.all(u_bin_shape_uncert * d_bin_shape_uncert <= 0.0), (
np.sum(u_bin_shape_uncert * d_bin_shape_uncert > 0.0),
u_bin_shape_uncert * d_bin_shape_uncert,
np.sum(u_bin_shape_uncert * d_bin_shape_uncert > 0.0),
u_bin_shape_uncert * d_bin_shape_uncert,
)
bin_shape_uncert_height: np.ndarray = np.abs(u_bin_shape_uncert) + np.abs(d_bin_shape_uncert)
# bin_shape_uncert_height: np.ndarray = np.abs(u_bin_shape_uncert) + np.abs(d_bin_shape_uncert)
bin_shape_uncert_lower_value: np.ndarray = np.minimum(u_bin_shape_uncert, d_bin_shape_uncert)
assert np.all(bin_shape_uncert_lower_value <= 0.0), (
np.sum(bin_shape_uncert_lower_value > 0.0),
......@@ -520,7 +532,8 @@ class SystematicsShapePlotter:
x=bin_mids,
height=scale_factor * np.abs(u_bin_shape_uncert),
width=bin_widths,
bottom=norm_bin_count + scale_factor * np.where(u_bin_shape_uncert < 0., u_bin_shape_uncert, np.zeros_like(u_bin_shape_uncert)),
bottom=norm_bin_count
+ scale_factor * np.where(u_bin_shape_uncert < 0.0, u_bin_shape_uncert, np.zeros_like(u_bin_shape_uncert)),
color=KITColors.kit_black,
hatch="//////",
fill=False,
......@@ -531,7 +544,8 @@ class SystematicsShapePlotter:
x=bin_mids,
height=scale_factor * np.abs(d_bin_shape_uncert),
width=bin_widths,
bottom=norm_bin_count + scale_factor * np.where(d_bin_shape_uncert < 0., d_bin_shape_uncert, np.zeros_like(d_bin_shape_uncert)),
bottom=norm_bin_count
+ scale_factor * np.where(d_bin_shape_uncert < 0.0, d_bin_shape_uncert, np.zeros_like(d_bin_shape_uncert)),
color=KITColors.kit_black,
hatch="\\\\\\\\\\\\",
fill=False,
......@@ -545,11 +559,11 @@ class SystematicsShapePlotter:
height=2 * bin_stat_uncert,
width=bin_widths,
bottom=-1.0 * bin_stat_uncert,
color=KITColors.dark_grey,
alpha=0.5,
color=KITColors.grey,
alpha=0.4,
fill=True,
lw=0,
label=f"stat.",
label="stat.",
)
ax2.hist(
x=[bin_mids, bin_mids],
......
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