def fit_subtracted_signal_dominated(
analysis: "correlations.Correlations") -> None:
""" Plot the subtracted signal dominated hist. """
# Setup
fig, ax = plt.subplots(figsize=(8, 6))
hists = analysis.correlation_hists_delta_phi_subtracted
h = hists.signal_dominated.hist
# Plot the subtracted hist
ax.errorbar(
h.x,
h.y,
yerr=h.errors,
label=f"Subtracted {hists.signal_dominated.type.display_str()}",
marker="o",
linestyle="",
)
# Plot the background uncertainty separately.
background_error = analysis.fit_object.calculate_background_function_errors(
h.x)
ax.fill_between(
h.x,
h.y - background_error,
h.y + background_error,
label="RP background uncertainty",
color=plot_base.AnalysisColors.fit,
)
# Line for comparison
ax.axhline(y=0, color="black", linestyle="dashed", zorder=1)
# Labels.
ax.set_xlabel(
labels.make_valid_latex_string(
hists.signal_dominated.axis.display_str()))
ax.set_ylabel(labels.make_valid_latex_string(
labels.delta_phi_axis_label()))
jet_pt_label = labels.jet_pt_range_string(analysis.jet_pt)
track_pt_label = labels.track_pt_range_string(analysis.track_pt)
ax.set_title(
fr"Subtracted 1D ${hists.signal_dominated.axis.display_str()}$,"
f" {analysis.reaction_plane_orientation.display_str()} event plane orient.,"
f" {jet_pt_label}, {track_pt_label}")
ax.legend(loc="upper right", frameon=False)
# Final adjustments
fig.tight_layout()
# Save plot and cleanup
plot_base.save_plot(analysis.output_info, fig,
f"jetH_delta_phi_{analysis.identifier}_subtracted")
plt.close(fig)
def test_delta_phi_axis_label(logging_mixin, include_normalized_by_n_trig,
expected):
""" Test for the delta phi axis label. """
label = labels.delta_phi_axis_label(
normalized_by_n_trig=include_normalized_by_n_trig)
assert label == expected
def plot_RP_fit(rp_fit: reaction_plane_fit.fit.ReactionPlaneFit,
inclusive_analysis: "correlations.Correlations",
ep_analyses: List[Tuple[Any, "correlations.Correlations"]],
output_info: analysis_objects.PlottingOutputWrapper,
output_name: str) -> None:
""" Basic plot of the reaction plane fit.
Args:
rp_fit: Reaction plane fit object.
inclusive_analysis: Inclusive analysis object. Mainly used for labeling.
ep_analyses: Event plane dependent correlation analysis objects.
output_info: Output information.
output_name: Name of the output plot.
Returns:
None. The plot will be saved.
"""
# Setup
n_components = len(rp_fit.components)
fig, axes = plt.subplots(2,
n_components,
sharey="row",
sharex=True,
gridspec_kw={"height_ratios": [3, 1]},
figsize=(3 * n_components, 6))
flat_axes = axes.flatten()
# Plot the fits on the upper panels.
_plot_rp_fit_components(rp_fit=rp_fit,
ep_analyses=ep_analyses,
axes=flat_axes[:n_components])
# Plot the residuals on the lower panels.
_plot_rp_fit_residuals(rp_fit=rp_fit,
ep_analyses=ep_analyses,
axes=flat_axes[n_components:])
# Define upper panel labels.
# In-plane
text = labels.track_pt_range_string(inclusive_analysis.track_pt)
text += "\n" + labels.constituent_cuts()
text += "\n" + labels.make_valid_latex_string(
inclusive_analysis.leading_hadron_bias.display_str())
_add_label_to_rpf_plot_axis(ax=flat_axes[0], label=text)
# Mid-plane
text = labels.make_valid_latex_string(
inclusive_analysis.alice_label.display_str())
text += "\n" + labels.system_label(
energy=inclusive_analysis.collision_energy,
system=inclusive_analysis.collision_system,
activity=inclusive_analysis.event_activity)
text += "\n" + labels.jet_pt_range_string(inclusive_analysis.jet_pt)
text += "\n" + labels.jet_finding()
_add_label_to_rpf_plot_axis(ax=flat_axes[1], label=text)
# Out-of-plane
#text = "Background: $0.8<|\Delta\eta|<1.2$"
#text += "\nSignal + Background: $|\Delta\eta|<0.6$"
#_add_label_to_rpf_plot_axis(ax = flat_axes[2], label = text)
# Inclusive
text = (r"\chi^{2}/\mathrm{NDF} = "
f"{rp_fit.fit_result.minimum_val:.1f}/{rp_fit.fit_result.nDOF} = "
f"{rp_fit.fit_result.minimum_val / rp_fit.fit_result.nDOF:.3f}")
_add_label_to_rpf_plot_axis(ax=flat_axes[2],
label=labels.make_valid_latex_string(text))
# Define lower panel labels.
for ax in flat_axes[n_components:]:
# Increase the frequency of major ticks to once every integer.
ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(base=1.0))
# Add axis labels
ax.set_xlabel(
labels.make_valid_latex_string(
inclusive_analysis.correlation_hists_delta_phi.
signal_dominated.axis.display_str()))
# Improve the viewable range for the lower panels.
# This value is somewhat arbitrarily selected, but seems to work well enough.
flat_axes[n_components].set_ylim(-0.2, 0.2)
# Specify shared y axis label
# Delta phi correlations first
flat_axes[0].set_ylabel(labels.delta_phi_axis_label())
# Then label the residual
flat_axes[n_components].set_ylabel("data - fit / fit")
# Final adjustments
fig.tight_layout()
# Reduce spacing between subplots
fig.subplots_adjust(hspace=0, wspace=0)
# Save plot and cleanup
plot_base.save_plot(output_info, fig, output_name)
plt.close(fig)
def rp_fit_subtracted(ep_analyses: List[Tuple[Any,
"correlations.Correlations"]],
inclusive_analysis: "correlations.Correlations",
output_info: analysis_objects.PlottingOutputWrapper,
output_name: str) -> None:
""" Basic plot of the reaction plane fit subtracted hists.
Args:
ep_analyses: Event plane dependent correlation analysis objects.
inclusive_analysis: Inclusive analysis object. Mainly used for labeling.
output_info: Output information.
output_name: Name of the output plot.
Returns:
None. The plot will be saved.
"""
# Setup
n_components = len(ep_analyses)
fig, axes = plt.subplots(
1,
n_components,
sharey="row",
sharex=True,
#gridspec_kw = {"height_ratios": [3, 1]},
figsize=(3 * n_components, 6))
flat_axes = axes.flatten()
# Plot the fits on the upper panels.
_plot_rp_fit_subtracted(ep_analyses=ep_analyses,
axes=flat_axes[:n_components])
# Define upper panel labels.
# In-plane
text = labels.track_pt_range_string(inclusive_analysis.track_pt)
text += "\n" + labels.constituent_cuts()
text += "\n" + labels.make_valid_latex_string(
inclusive_analysis.leading_hadron_bias.display_str())
_add_label_to_rpf_plot_axis(ax=flat_axes[0], label=text)
# Mid-plane
text = labels.make_valid_latex_string(
inclusive_analysis.alice_label.display_str())
text += "\n" + labels.system_label(
energy=inclusive_analysis.collision_energy,
system=inclusive_analysis.collision_system,
activity=inclusive_analysis.event_activity)
text += "\n" + labels.jet_pt_range_string(inclusive_analysis.jet_pt)
text += "\n" + labels.jet_finding()
_add_label_to_rpf_plot_axis(ax=flat_axes[1], label=text)
# Out-of-plane
#text = "Background: $0.8<|\Delta\eta|<1.2$"
#text += "\nSignal + Background: $|\Delta\eta|<0.6$"
#_add_label_to_rpf_plot_axis(ax = flat_axes[2], label = text)
_add_label_to_rpf_plot_axis(ax=flat_axes[2],
label=labels.make_valid_latex_string(text))
for ax in flat_axes:
# Increase the frequency of major ticks to once every integer.
ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(base=1.0))
# Set label
ax.set_xlabel(labels.make_valid_latex_string(r"\Delta\varphi"))
flat_axes[0].set_ylabel(
labels.make_valid_latex_string(labels.delta_phi_axis_label()))
#jet_pt_label = labels.jet_pt_range_string(inclusive_analysis.jet_pt)
#track_pt_label = labels.track_pt_range_string(inclusive_analysis.track_pt)
#ax.set_title(fr"Subtracted 1D ${inclusive_analysis.correlation_hists_delta_phi_subtracted.signal_dominated.axis.display_str()}$,"
# f" {inclusive_analysis.reaction_plane_orientation.display_str()} event plane orient.,"
# f" {jet_pt_label}, {track_pt_label}")
ax.legend(loc="upper right")
# Final adjustments
fig.tight_layout()
# Reduce spacing between subplots
fig.subplots_adjust(hspace=0, wspace=0)
# Save plot and cleanup
plot_base.save_plot(
output_info, fig,
f"jetH_delta_phi_{inclusive_analysis.identifier}_rp_subtracted")
plt.close(fig)
def simplified_mixed_event_comparison(output_info: analysis_objects.PlottingOutputWrapper,
# For labeling purposes
output_name: str, eta_limits: Sequence[float],
jet_pt_title: str, track_pt_title: str,
# Mixed event 1D hist
mixed_event_1D: histogram.Histogram1D,
max_moving_avg: float,
fit_1D: float, fit_2D: float) -> None:
""" Simplified mixed event comparison.
Args:
output_info: Standard information needed to store the output.
output_name: Output name.
eta_limits: Eta limits.
jet_pt_title: Jet pt title for labeling.
track_pt_title: Track pt title for labeling.
mixed_event_1D: 1D mixed event.
max_moving_avg: Maximum of the moving average (nominal value).
fit_1D: Max value from a 1D fit.
fit_2D: Max value from a 2D fit.
Returns:
None. The comparison is plotted.
"""
# Setup
fig, ax = plt.subplots(figsize = (8, 6))
hist = histogram.Histogram1D.from_existing_hist(mixed_event_1D)
# Plot the data
ax.errorbar(
hist.x, hist.y, yerr = hist.errors, linestyle = "", marker = "o",
label = "Mixed event",
)
# Plot the lines
ax.axhline(
max_moving_avg, xmin = 0.4, xmax = 1,
label = r"Moving avg. (size $\pi$)", color = "tab:orange"
)
ax.axhline(
fit_1D, xmin = 0.5, xmax = 1,
label = r"1D fit ($\pi/2-3\pi/2$)", color = "tab:purple"
)
ax.axhline(
fit_2D, xmin = 0.5, xmax = 1,
label = r"2D fit ($\pi/2-3\pi/2$)", linestyle = "--", color = "tab:purple"
)
# Label delta eta range.
ax.text(
0.05, 0.95, fr"$|\Delta\eta| < {eta_limits[1]}$", horizontalalignment = "left",
verticalalignment = "top", multialignment = "left",
transform = ax.transAxes
)
# Legend and Labels for the plot
ax.legend(loc = "lower right", frameon = False)
ax.set_title(f"ME norm. for {jet_pt_title}, {track_pt_title}")
ax.set_ylabel(labels.delta_phi_axis_label())
ax.set_xlabel(r"$\Delta\varphi$")
# Final adjustments
plt.tight_layout()
# Cleanup and save
plot_base.save_plot(output_info, fig, output_name)
plt.close(fig)
def mixed_event_normalization(output_info: analysis_objects.PlottingOutputWrapper,
# For labeling purposes
output_name: str, eta_limits: Sequence[float], jet_pt_title: str, track_pt_title: str,
# Basic data
lin_space: np.ndarray, peak_finding_hist_array: np.ndarray, # noqa: E241
lin_space_rebin: np.ndarray, peak_finding_hist_array_rebin: np.ndarray,
# CWT
peak_locations: np.ndarray, peak_locations_rebin: np.ndarray,
# Moving Average
max_moving_avg: float, max_moving_avg_rebin: float,
# Smoothed gaussian
lin_space_resample: np.ndarray, smoothed_array: np.ndarray, max_smoothed_moving_avg: float,
# Linear fits
max_linear_fit_1d: float, max_linear_fit_1d_rebin: float,
max_linear_fit_2d: float, max_linear_fit_2d_rebin: float) -> None:
# Make the actual plot
fig, ax = plt.subplots(figsize = (8, 6))
# Add additional y margin at the bottom so the legend will fit a bit better
# Cannot do asymmetric padding via `ax.set_ymargin()`, so we'll do it by hand
# See: https://stackoverflow.com/a/42804403
data_min = min(peak_finding_hist_array.min(), peak_finding_hist_array_rebin.min())
data_max = max(peak_finding_hist_array.max(), peak_finding_hist_array_rebin.max())
y_min = data_min - 0.5 * (data_max - data_min)
y_max = data_max + 0.12 * (data_max - data_min)
ax.set_ylim(y_min, y_max)
# Can either plot the hist or the array
# Hist based on: https://stackoverflow.com/a/8553614
#ax.hist(lin_space, weights=peak_finding_hist_array, bins=len(peak_finding_hist_array))
# If plotting the hist, it's best to set the y axis limits to make it easier to view
#ax.set_ylim(ymin=.95*min(peak_finding_hist_array), ymax=1.05*max(peak_finding_hist_array))
# Plot array
ax.plot(lin_space, peak_finding_hist_array, label = "ME")
ax.plot(lin_space_rebin, peak_finding_hist_array_rebin, label = "ME rebin")
# Peak finding
# Set zorder of 10 to ensure that the stars are always visible
plot_array_peak = ax.plot(
lin_space[peak_locations],
peak_finding_hist_array[peak_locations],
marker = "*", markersize = 10, linestyle = "None",
label = "CWT", zorder = 10
)
plot_array_rebin_peak = ax.plot(
lin_space_rebin[peak_locations_rebin],
peak_finding_hist_array_rebin[peak_locations_rebin],
marker = "*", markersize = 10, linestyle = "None",
label = "CWT rebin", zorder = 10
)
# Moving average
ax.axhline(max_moving_avg, color = plot_array_peak[0].get_color(), label = r"Mov. avg. (size $\pi$)")
ax.axhline(max_moving_avg_rebin, color = plot_array_rebin_peak[0].get_color(), linestyle = "--", label = "Mov. avg. rebin")
# Gaussian
# Use a mask so the range doesn't get extremely distorted when the interpolation drops around the edges
mask = np.where(np.logical_and(lin_space_resample > -0.3 * np.pi, lin_space_resample < 1.3 * np.pi))
plot_gaussian = ax.plot(lin_space_resample[mask], smoothed_array[mask], label = "Gauss. smooth")
ax.axhline(max_smoothed_moving_avg, color = plot_gaussian[0].get_color(), linestyle = "--", label ="Gauss. mov. avg")
#ax.axhline(max_smoothed, color = plot_gaussian[0].get_color(), linestyle = ":", label = "Gauss. max")
# Linear fits
ax.axhline(max_linear_fit_1d, color = "g", label = "1D fit")
ax.axhline(max_linear_fit_1d_rebin, color = "g", linestyle = "--", label = "1D fit rebin")
ax.axhline(max_linear_fit_2d, color = "b", label = "2D fit")
ax.axhline(max_linear_fit_2d_rebin, color = "b", linestyle = "--", label = "2D fit rebin")
ax.text(
0.05, 0.95, fr"$|\Delta\eta| < {eta_limits[1]}$", horizontalalignment = "left",
verticalalignment = "top", multialignment = "left",
transform = ax.transAxes
)
# Legend and Labels for the plot
#ax.set_ymargin = 0.01
ax.legend(loc = "lower left", ncol = 3, frameon = False)
#ax.legend(bbox_to_anchor=(0,1.02,1,0.2), loc="lower left",
# ncol=3, mode="expand", borderaxespad=0)
ax.set_title(f"ME norm. for {jet_pt_title}, {track_pt_title}")
ax.set_ylabel(labels.delta_phi_axis_label())
ax.set_xlabel(r"$\Delta\varphi$")
# Final adjustments
plt.tight_layout()
# Cleanup and save
plot_base.save_plot(output_info, fig, output_name)
plt.close(fig)
def delta_phi_with_gaussians(analysis: "correlations.Correlations") -> None:
""" Plot the subtracted delta phi correlation with gaussian fits to the near and away side. """
# Setup
fig, ax = plt.subplots(figsize=(8, 6))
correlation = analysis.correlation_hists_delta_phi_subtracted.signal_dominated
h = correlation.hist
# First we plot the data
ax.errorbar(
h.x,
h.y,
yerr=h.errors,
marker="o",
linestyle="",
label=f"{correlation.type.display_str()}",
)
# Plot the fit.
for attribute_name, width_obj in analysis.widths_delta_phi:
# Setup
# Convert the attribute name to display better. Ex: "near_side" -> "Near side"
attribute_display_name = attribute_name.replace("_", " ").capitalize()
# We only want to plot the fit over the range that it was fit.
restricted_range = (h.x > width_obj.fit_object.fit_range.min) & (
h.x < width_obj.fit_object.fit_range.max)
x = h.x[restricted_range]
# Plot the fit
gauss = width_obj.fit_object(x,
**width_obj.fit_result.values_at_minimum)
fit_plot = ax.plot(
x,
gauss,
label=
fr"{attribute_display_name} gaussian fit: $\mu = $ {width_obj.mean:.2f}"
fr", $\sigma = $ {width_obj.width:.2f}",
)
# Fill in the error band.
error = width_obj.fit_object.calculate_errors(x=x)
ax.fill_between(
x,
gauss - error,
gauss + error,
facecolor=fit_plot[0].get_color(),
alpha=0.5,
)
# This means that we extracted values from the RP fit. Let's also plot them for comparison
if width_obj.fit_args != {}:
args = dict(width_obj.fit_result.values_at_minimum)
args.update({
# Help out mypy...
k: cast(float, v)
for k, v in width_obj.fit_args.items() if "error_" not in k
})
rpf_gauss = width_obj.fit_object(x, **args)
rp_fit_plot = ax.plot(
x,
rpf_gauss,
label=
fr"RPF {attribute_display_name} gaussian fit: $\mu = $ {width_obj.mean:.2f}"
fr", $\sigma = $ {width_obj.fit_args['width']:.2f}",
)
# Fill in the error band.
# NOTE: Strictly speaking, this error band isn't quite right (since it is dependent on the fit result
# of the actual width fit), but I think it's fine for these purposes.
error = width_obj.fit_object.calculate_errors(x=x)
ax.fill_between(
x,
rpf_gauss - error,
rpf_gauss + error,
facecolor=rp_fit_plot[0].get_color(),
alpha=0.5,
)
# Labels.
ax.set_xlabel(
labels.make_valid_latex_string(correlation.axis.display_str()))
ax.set_ylabel(labels.make_valid_latex_string(
labels.delta_phi_axis_label()))
jet_pt_label = labels.jet_pt_range_string(analysis.jet_pt)
track_pt_label = labels.track_pt_range_string(analysis.track_pt)
ax.set_title(
fr"Subtracted 1D ${correlation.axis.display_str()}$,"
f" {analysis.reaction_plane_orientation.display_str()} event plane orient.,"
f" {jet_pt_label}, {track_pt_label}")
ax.legend(loc="upper right")
# Final adjustments
fig.tight_layout()
# Save plot and cleanup
plot_base.save_plot(
analysis.output_info, fig,
f"jetH_delta_phi_{analysis.identifier}_width_signal_dominated_fit")
plt.close(fig)
