Functions
def	load_selected_matrix (root_file, matrix_name, selected_vars, with_errors=True)

def	plot_matrix (selected_matrix, errors_matrix, selected_vars, title, output_name, colorbar_label)

Variables
	parser

	description

	type

	str

	help

	nargs

	args

	file

	rates

	rates_err

	selected_vars

	with_errors

	counts

	_

	topo

	topo_err

Function Documentation

◆ load_selected_matrix()

def L1TopoRatesCalculator_submatrix_plotter.load_selected_matrix	(	root_file,
		matrix_name,
		selected_vars,
		with_errors = `True`
	)

Definition at line 10 of file L1TopoRatesCalculator_submatrix_plotter.py.

 def load_selected_matrix(root_file, matrix_name, selected_vars, with_errors=True):
     matrix = root_file.Get(matrix_name)
     if not matrix:
         raise RuntimeError(f"Matrix '{matrix_name}' not found in ROOT file.")
  
     n = matrix.GetNbinsX()
     all_vars = [matrix.GetXaxis().GetBinLabel(i) for i in range(1, n+1)]
     indices = [all_vars.index(var) for var in selected_vars]
  
     selected_matrix = np.zeros((len(indices), len(indices)))
     errors_matrix = np.zeros((len(indices), len(indices))) if with_errors else None
  
     for i, ix in enumerate(indices):
         for j, iy in enumerate(indices):
             selected_matrix[i, j] = matrix.GetBinContent(ix+1, iy+1)
             if with_errors:
                 errors_matrix[i, j] = matrix.GetBinError(ix+1, iy+1)
  
     return selected_matrix, errors_matrix
  

◆ plot_matrix()

def L1TopoRatesCalculator_submatrix_plotter.plot_matrix	(	selected_matrix,
		errors_matrix,
		selected_vars,
		title,
		output_name,
		colorbar_label
	)

Definition at line 30 of file L1TopoRatesCalculator_submatrix_plotter.py.

 def plot_matrix(selected_matrix, errors_matrix, selected_vars, title, output_name, colorbar_label):
     min_value = np.min(selected_matrix)
     max_value = np.max(selected_matrix)
  
     fig, ax = plt.subplots(figsize=(46, 44))
     cax = ax.matshow(selected_matrix, cmap='coolwarm', norm=Normalize(vmin=min_value, vmax=max_value))
  
     for (i, j), val in np.ndenumerate(selected_matrix):
         if errors_matrix is not None:
             error = errors_matrix[i, j]
             text = f'{val:.2f}\n±{error:.2f}'
         else:
             text = f'{val:.2f}'
         ax.text(j, i, text, ha='center', va='center', color='black', fontsize=25)
  
     ax.set_xticks(np.arange(len(selected_vars)))
     ax.set_yticks([])
     ax.set_xticklabels(selected_vars, rotation=90, fontsize=25)
     ax.set_yticklabels([])
     plt.xticks(rotation=45)
     for tick in ax.get_xticklabels():
         tick.set_horizontalalignment('left')
  
     colorbar = fig.colorbar(cax)
     colorbar.ax.tick_params(labelsize=25)
     colorbar.set_label(colorbar_label, fontsize=25)
  
     plt.title(title, fontsize=30)
     plt.savefig(output_name, bbox_inches='tight')
     plt.close()
  