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 from dataclasses import dataclass
 from typing import Tuple
 
 import numpy as np
 from matplotlib import pyplot as plt
 from scipy.optimize import curve_fit
 
 from core.constants import N_CELLS_Z, N_CELLS_R, VALID_DIR, SIZE_Z, SIZE_R, HISTOGRAM_TYPE, FULL_SIM_HISTOGRAM_COLOR, \
     ML_SIM_HISTOGRAM_COLOR, FULL_SIM_GAUSSIAN_COLOR, ML_SIM_GAUSSIAN_COLOR
 from utils.observables import LongitudinalProfile, ProfileType, Profile, Energy
 
 plt.rcParams.update({"font.size": 22})
 
 
 @dataclass
 class Plotter:
     """ An abstract class defining interface of all plotters.
 
     Do not use this class directly. Use ProfilePlotter or EnergyPlotter instead.
 
     Attributes:
         _particle_energy: An integer which is energy of the primary particle in GeV units.
         _particle_angle: An integer which is an angle of the primary particle in degrees.
         _geometry: A string which is a name of the calorimeter geometry (e.g. SiW, SciPb).
 
     """
     _particle_energy: int
     _particle_angle: int
     _geometry: str
 
     def plot_and_save(self):
         pass
 
 
 def _gaussian(x: np.ndarray, a: float, mu: float, sigma: float) -> np.ndarray:
     """ Computes a value of a Gaussian.
 
     Args:
         x: An argument of a function.
         a: A scaling parameter.
         mu: A mean.
         sigma: A variance.
 
     Returns:
         A value of a function for given arguments.
 
     """
     return a * np.exp(-((x - mu)**2 / (2 * sigma**2)))
 
 
 def _best_fit(data: np.ndarray,
               bins: np.ndarray,
               hist: bool = False) -> Tuple[np.ndarray, np.ndarray]:
     """ Finds estimated shape of a Gaussian using Use non-linear least squares.
 
     Args:
         data: A numpy array with values of observables from multiple events.
         bins: A numpy array specifying histogram bins.
         hist: If histogram is calculated. Then data is the frequencies.
 
     Returns:
         A tuple of two lists. Xs and Ys of predicted curve.
 
     """
     # Calculate histogram.
     if not hist:
         hist, _ = np.histogram(data, bins)
     else:
         hist = data
 
     # Choose only those bins which are nonzero. Nonzero() return a tuple of arrays. In this case it has a length = 1,
     # hence we are interested in its first element.
     indices = hist.nonzero()[0]
 
     # Based on previously chosen nonzero bin, calculate position of xs and ys_bar (true values) which will be used in
     # fitting procedure. Len(bins) == len(hist + 1), so we choose middles of bins as xs.
     bins_middles = (bins[:-1] + bins[1:]) / 2
     xs = bins_middles[indices]
     ys_bar = hist[indices]
 
     # Set initial parameters for curve fitter.
     a0 = np.max(ys_bar)
     mu0 = np.mean(xs)
     sigma0 = np.var(xs)
 
     # Fit a Gaussian to the prepared data.
     (a, mu, sigma), _ = curve_fit(f=_gaussian,
                                   xdata=xs,
                                   ydata=ys_bar,
                                   p0=[a0, mu0, sigma0],
                                   method="trf",
                                   maxfev=1000)
 
     # Calculate values of an approximation in given points and return values.
     ys = _gaussian(xs, a, mu, sigma)
     return xs, ys
 
 
 @dataclass
 class ProfilePlotter(Plotter):
     """ Plotter responsible for preparing plots of profiles and their first and second moments.
 
     Attributes:
         _full_simulation: A numpy array representing a profile of data generated by Geant4.
         _ml_simulation: A numpy array representing a profile of data generated by ML model.
         _plot_gaussian: A boolean. Decides whether first and second moment should be plotted as a histogram or
             a fitted gaussian.
         _profile_type: An enum. A profile can be either lateral or longitudinal.
 
     """
     _full_simulation: Profile
     _ml_simulation: Profile
     _plot_gaussian: bool = False
 
     def __post_init__(self):
         # Check if profiles are either both longitudinal or lateral.
         full_simulation_type = type(self._full_simulation)
         ml_generation_type = type(self._ml_simulation)
         assert full_simulation_type == ml_generation_type, "Both profiles within a ProfilePlotter must be the same " \
                                                            "type."
 
         # Set an attribute with profile type.
         if full_simulation_type == LongitudinalProfile:
             self._profile_type = ProfileType.LONGITUDINAL
         else:
             self._profile_type = ProfileType.LATERAL
 
     def _plot_and_save_customizable_histogram(
             self,
             full_simulation: np.ndarray,
             ml_simulation: np.ndarray,
             bins: np.ndarray,
             xlabel: str,
             observable_name: str,
             plot_profile: bool = False,
             y_log_scale: bool = False) -> None:
         """ Prepares and saves a histogram for a given pair of observables.
 
         Args:
             full_simulation: A numpy array of observables coming from full simulation.
             ml_simulation: A numpy array of observables coming from ML simulation.
             bins: A numpy array specifying histogram bins.
             xlabel: A string. Name of x-axis on the plot.
             observable_name: A string. Name of plotted observable.
             plot_profile: A boolean. If set to True, full_simulation and ml_simulation are histogram weights while x is
                 defined by the number of layers. This means that in order to plot histogram (and gaussian), one first
                 need to create a data repeating each layer or R index appropriate number of times. Should be set to True
                 only while plotting profiles not first or second moments.
             y_log_scale: A boolean. Used log scale on y-axis is set to True.
 
         Returns:
             None.
 
         """
         fig, axes = plt.subplots(2,
                                  1,
                                  figsize=(15, 10),
                                  clear=True,
                                  sharex="all")
 
         # Plot histograms.
         if plot_profile:
             # We already have the bins (layers) and freqencies (energies),
             # therefore directly plotting a step plot + lines instead of a hist plot.
             axes[0].step(bins[:-1],
                          full_simulation,
                          label="FullSim",
                          color=FULL_SIM_HISTOGRAM_COLOR)
             axes[0].step(bins[:-1],
                          ml_simulation,
                          label="MLSim",
                          color=ML_SIM_HISTOGRAM_COLOR)
             axes[0].vlines(x=bins[0],
                            ymin=0,
                            ymax=full_simulation[0],
                            color=FULL_SIM_HISTOGRAM_COLOR)
             axes[0].vlines(x=bins[-2],
                            ymin=0,
                            ymax=full_simulation[-1],
                            color=FULL_SIM_HISTOGRAM_COLOR)
             axes[0].vlines(x=bins[0],
                            ymin=0,
                            ymax=ml_simulation[0],
                            color=ML_SIM_HISTOGRAM_COLOR)
             axes[0].vlines(x=bins[-2],
                            ymin=0,
                            ymax=ml_simulation[-1],
                            color=ML_SIM_HISTOGRAM_COLOR)
             axes[0].set_ylim(0, None)
 
             # For using it later for the ratios.
             energy_full_sim, energy_ml_sim = full_simulation, ml_simulation
         else:
             energy_full_sim, _, _ = axes[0].hist(
                 x=full_simulation,
                 bins=bins,
                 label="FullSim",
                 histtype=HISTOGRAM_TYPE,
                 color=FULL_SIM_HISTOGRAM_COLOR)
             energy_ml_sim, _, _ = axes[0].hist(x=ml_simulation,
                                                bins=bins,
                                                label="MLSim",
                                                histtype=HISTOGRAM_TYPE,
                                                color=ML_SIM_HISTOGRAM_COLOR)
 
         # Plot Gaussians if needed.
         if self._plot_gaussian:
             if plot_profile:
                 (xs_full_sim, ys_full_sim) = _best_fit(full_simulation,
                                                        bins,
                                                        hist=True)
                 (xs_ml_sim, ys_ml_sim) = _best_fit(ml_simulation,
                                                    bins,
                                                    hist=True)
             else:
                 (xs_full_sim, ys_full_sim) = _best_fit(full_simulation, bins)
                 (xs_ml_sim, ys_ml_sim) = _best_fit(ml_simulation, bins)
             axes[0].plot(xs_full_sim,
                          ys_full_sim,
                          color=FULL_SIM_GAUSSIAN_COLOR,
                          label="FullSim")
             axes[0].plot(xs_ml_sim,
                          ys_ml_sim,
                          color=ML_SIM_GAUSSIAN_COLOR,
                          label="MLSim")
 
         if y_log_scale:
             axes[0].set_yscale("log")
         axes[0].legend(loc="best")
         axes[0].set_xlabel(xlabel)
         axes[0].set_ylabel("Energy [Mev]")
         axes[0].set_title(
             f" $e^-$, {self._particle_energy} [GeV], {self._particle_angle}$^{{\circ}}$, {self._geometry}"
         )
 
         # Calculate ratios.
         ratio = np.divide(energy_ml_sim,
                           energy_full_sim,
                           out=np.ones_like(energy_ml_sim),
                           where=(energy_full_sim != 0))
         # Since len(bins) == 1 + data, we calculate middles of bins as xs.
         bins_middles = (bins[:-1] + bins[1:]) / 2
         axes[1].plot(bins_middles, ratio, "-o")
         axes[1].set_xlabel(xlabel)
         axes[1].set_ylabel("MLSim/FullSim")
         axes[1].axhline(y=1, color="black")
         plt.savefig(
             f"{VALID_DIR}/{observable_name}_Geo_{self._geometry}_E_{self._particle_energy}_"
             + f"Angle_{self._particle_angle}.png")
         plt.clf()
 
     def _plot_profile(self) -> None:
         """ Plots profile of an observable.
 
         Returns:
             None.
 
         """
         full_simulation_profile = self._full_simulation.calc_profile()
         ml_simulation_profile = self._ml_simulation.calc_profile()
         if self._profile_type == ProfileType.LONGITUDINAL:
             # matplotlib will include the right-limit for the last bar,
             # hence extending by 1.
             bins = np.linspace(0, N_CELLS_Z, N_CELLS_Z + 1)
             observable_name = "LongProf"
             xlabel = "Layer index"
         else:
             bins = np.linspace(0, N_CELLS_R, N_CELLS_R + 1)
             observable_name = "LatProf"
             xlabel = "R index"
         self._plot_and_save_customizable_histogram(full_simulation_profile,
                                                    ml_simulation_profile,
                                                    bins,
                                                    xlabel,
                                                    observable_name,
                                                    plot_profile=True)
 
     def _plot_first_moment(self) -> None:
         """ Plots and saves a first moment of an observable's profile.
 
         Returns:
             None.
 
         """
         full_simulation_first_moment = self._full_simulation.calc_first_moment(
         )
         ml_simulation_first_moment = self._ml_simulation.calc_first_moment()
         if self._profile_type == ProfileType.LONGITUDINAL:
             xlabel = "$<\lambda> [mm]$"
             observable_name = "LongFirstMoment"
             bins = np.linspace(0, 0.4 * N_CELLS_Z * SIZE_Z, 128)
         else:
             xlabel = "$<r> [mm]$"
             observable_name = "LatFirstMoment"
             bins = np.linspace(0, 0.75 * N_CELLS_R * SIZE_R, 128)
 
         self._plot_and_save_customizable_histogram(
             full_simulation_first_moment, ml_simulation_first_moment, bins,
             xlabel, observable_name)
 
     def _plot_second_moment(self) -> None:
         """ Plots and saves a second moment of an observable's profile.
 
         Returns:
             None.
 
         """
         full_simulation_second_moment = self._full_simulation.calc_second_moment(
         )
         ml_simulation_second_moment = self._ml_simulation.calc_second_moment()
         if self._profile_type == ProfileType.LONGITUDINAL:
             xlabel = "$<\lambda^{2}> [mm^{2}]$"
             observable_name = "LongSecondMoment"
             bins = np.linspace(0, pow(N_CELLS_Z * SIZE_Z, 2) / 35., 128)
         else:
             xlabel = "$<r^{2}> [mm^{2}]$"
             observable_name = "LatSecondMoment"
             bins = np.linspace(0, pow(N_CELLS_R * SIZE_R, 2) / 8., 128)
 
         self._plot_and_save_customizable_histogram(
             full_simulation_second_moment, ml_simulation_second_moment, bins,
             xlabel, observable_name)
 
     def plot_and_save(self) -> None:
         """ Main plotting function.
 
         Calls private methods and prints the information about progress.
 
         Returns:
             None.
 
         """
         if self._profile_type == ProfileType.LONGITUDINAL:
             profile_type_name = "longitudinal"
         else:
             profile_type_name = "lateral"
         print(f"Plotting the {profile_type_name} profile...")
         self._plot_profile()
         print(f"Plotting the first moment of {profile_type_name} profile...")
         self._plot_first_moment()
         print(f"Plotting the second moment of {profile_type_name} profile...")
         self._plot_second_moment()
 
 
 @dataclass
 class EnergyPlotter(Plotter):
     """ Plotter responsible for preparing plots of profiles and their first and second moments.
 
     Attributes:
         _full_simulation: A numpy array representing a profile of data generated by Geant4.
         _ml_simulation: A numpy array representing a profile of data generated by ML model.
 
     """
     _full_simulation: Energy
     _ml_simulation: Energy
 
     def _plot_total_energy(self, y_log_scale=True) -> None:
         """ Plots and saves a histogram with total energy detected in an event.
 
         Args:
             y_log_scale: A boolean. Used log scale on y-axis is set to True.
 
         Returns:
             None.
 
         """
         full_simulation_total_energy = self._full_simulation.calc_total_energy(
         )
         ml_simulation_total_energy = self._ml_simulation.calc_total_energy()
 
         plt.figure(figsize=(12, 8))
         bins = np.linspace(
             np.min(full_simulation_total_energy) -
             np.min(full_simulation_total_energy) * 0.05,
             np.max(full_simulation_total_energy) +
             np.max(full_simulation_total_energy) * 0.05, 50)
         plt.hist(x=full_simulation_total_energy,
                  histtype=HISTOGRAM_TYPE,
                  label="FullSim",
                  bins=bins,
                  color=FULL_SIM_HISTOGRAM_COLOR)
         plt.hist(x=ml_simulation_total_energy,
                  histtype=HISTOGRAM_TYPE,
                  label="MLSim",
                  bins=bins,
                  color=ML_SIM_HISTOGRAM_COLOR)
         plt.legend(loc="upper left")
         if y_log_scale:
             plt.yscale("log")
         plt.xlabel("Energy [MeV]")
         plt.ylabel("# events")
         plt.title(
             f" $e^-$, {self._particle_energy} [GeV], {self._particle_angle}$^{{\circ}}$, {self._geometry} "
         )
         plt.savefig(
             f"{VALID_DIR}/E_tot_Geo_{self._geometry}_E_{self._particle_energy}_Angle_{self._particle_angle}.png"
         )
         plt.clf()
 
     def _plot_cell_energy(self) -> None:
         """ Plots and saves a histogram with number of detector's cells across whole
         calorimeter with particular energy detected.
 
         Returns:
             None.
 
         """
         full_simulation_cell_energy = self._full_simulation.calc_cell_energy()
         ml_simulation_cell_energy = self._ml_simulation.calc_cell_energy()
 
         log_full_simulation_cell_energy = np.log10(
             full_simulation_cell_energy,
             out=np.zeros_like(full_simulation_cell_energy),
             where=(full_simulation_cell_energy != 0))
         log_ml_simulation_cell_energy = np.log10(
             ml_simulation_cell_energy,
             out=np.zeros_like(ml_simulation_cell_energy),
             where=(ml_simulation_cell_energy != 0))
         plt.figure(figsize=(12, 8))
         bins = np.linspace(-4, 1, 1000)
         plt.hist(x=log_full_simulation_cell_energy,
                  bins=bins,
                  histtype=HISTOGRAM_TYPE,
                  label="FullSim",
                  color=FULL_SIM_HISTOGRAM_COLOR)
         plt.hist(x=log_ml_simulation_cell_energy,
                  bins=bins,
                  histtype=HISTOGRAM_TYPE,
                  label="MLSim",
                  color=ML_SIM_HISTOGRAM_COLOR)
         plt.xlabel("log10(E/MeV)")
         plt.ylim(bottom=1)
         plt.yscale("log")
         plt.ylim(bottom=1)
         plt.ylabel("# entries")
         plt.title(
             f" $e^-$, {self._particle_energy} [GeV], {self._particle_angle}$^{{\circ}}$, {self._geometry} "
         )
         plt.grid(True)
         plt.legend(loc="upper left")
         plt.savefig(
             f"{VALID_DIR}/E_cell_Geo_{self._geometry}_E_{self._particle_energy}_Angle_{self._particle_angle}.png"
         )
         plt.clf()
 
     def _plot_energy_per_layer(self):
         """ Plots and saves N_CELLS_Z histograms with total energy detected in particular layers.
 
         Returns:
             None.
 
         """
         full_simulation_energy_per_layer = self._full_simulation.calc_energy_per_layer(
         )
         ml_simulation_energy_per_layer = self._ml_simulation.calc_energy_per_layer(
         )
 
         number_of_plots_in_row = 9
         number_of_plots_in_column = 5
 
         bins = np.linspace(np.min(full_simulation_energy_per_layer - 10),
                            np.max(full_simulation_energy_per_layer + 10), 25)
 
         fig, ax = plt.subplots(number_of_plots_in_column,
                                number_of_plots_in_row,
                                figsize=(20, 15),
                                sharex="all",
                                sharey="all",
                                constrained_layout=True)
 
         for layer_nb in range(N_CELLS_Z):
             i = layer_nb // number_of_plots_in_row
             j = layer_nb % number_of_plots_in_row
 
             ax[i][j].hist(full_simulation_energy_per_layer[:, layer_nb],
                           histtype=HISTOGRAM_TYPE,
                           label="FullSim",
                           bins=bins,
                           color=FULL_SIM_HISTOGRAM_COLOR)
             ax[i][j].hist(ml_simulation_energy_per_layer[:, layer_nb],
                           histtype=HISTOGRAM_TYPE,
                           label="MLSim",
                           bins=bins,
                           color=ML_SIM_HISTOGRAM_COLOR)
             ax[i][j].set_title(f"Layer {layer_nb}", fontsize=13)
             ax[i][j].set_yscale("log")
             ax[i][j].tick_params(axis='both', which='major', labelsize=10)
 
         fig.supxlabel("Energy [MeV]", fontsize=14)
         fig.supylabel("# entries", fontsize=14)
         fig.suptitle(
             f" $e^-$, {self._particle_energy} [GeV], {self._particle_angle}$^{{\circ}}$, {self._geometry} "
         )
 
         # Take legend from one plot and make it a global legend.
         handles, labels = ax[0][0].get_legend_handles_labels()
         fig.legend(handles, labels, bbox_to_anchor=(1.15, 0.5))
 
         plt.savefig(
             f"{VALID_DIR}/E_layer_Geo_{self._geometry}_E_{self._particle_energy}_Angle_{self._particle_angle}.png",
             bbox_inches="tight")
         plt.clf()
 
     def plot_and_save(self):
         """ Main plotting function.
 
         Calls private methods and prints the information about progress.
 
         Returns:
             None.
 
         """
         print("Plotting total energy...")
         self._plot_total_energy()
         print("Plotting cell energy...")
         self._plot_cell_energy()
         print("Plotting energy per layer...")
         self._plot_energy_per_layer()

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