Functions
def	plot_correlation_matrix (file_name, matrix_name, selected_vars, output_name)

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	parser

	description

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	str

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Function Documentation

◆ plot_correlation_matrix()

def L1TopoRatesCalculator_submatrix_plotter.plot_correlation_matrix	(	file_name,
		matrix_name,
		selected_vars,
		output_name
	)

Definition at line 9 of file L1TopoRatesCalculator_submatrix_plotter.py.

 def plot_correlation_matrix(file_name, matrix_name, selected_vars, output_name):
     # Open the ROOT file
     root_file = ROOT.TFile.Open(file_name)
     
     # Retrieve the correlation matrix
     rates_matrix = root_file.Get(matrix_name)
     
     # Extract the list of variables
     n = rates_matrix.GetNbinsX()  # Assuming the number of bins is the same in both X and Y axes
     variables = [rates_matrix.GetXaxis().GetBinLabel(i) for i in range(1, n+1)]
    
     # Filter the selected variables
     indices = [variables.index(var) for var in selected_vars]
     
     # Create a numpy array to store the selected values
     selected_matrix = np.zeros((len(indices), len(indices)))
     errors_matrix = np.zeros((len(indices), len(indices))) 
     
     for i, ix in enumerate(indices):
         for j, iy in enumerate(indices):
             selected_matrix[i, j] = rates_matrix.GetBinContent(ix+1, iy+1)
             errors_matrix[i, j] = rates_matrix.GetBinError(ix+1, iy+1) 
     # Find the minimum and maximum values in the selected matrix
     min_value = np.min(selected_matrix)
     max_value = np.max(selected_matrix)
     
     # Create the plot
     fig, ax = plt.subplots(figsize=(24, 22))
     cax = ax.matshow(selected_matrix, cmap='coolwarm', norm=Normalize(vmin=min_value, vmax=max_value))
     
     # Add text inside the cells
     for (i, j), val in np.ndenumerate(selected_matrix):
         error = errors_matrix[i, j]
         ax.text(j, i, f'{val:.2f}\n±{error:.2f}', ha='center', va='center', color='black', fontsize=25)
     
     # Set the axis labels
     ax.set_xticks(np.arange(len(indices)))
     ax.set_yticks([])
     ax.set_xticklabels([selected_vars[i] for i in range(len(indices))], rotation=90, fontsize=25)
     ax.set_yticklabels([])
     plt.xticks(rotation=45)
  
     # Adjust labels to align at the start of each bin
     for tick in ax.get_xticklabels():
         tick.set_horizontalalignment('left')  # Align left
     # Add a color bar
     colorbar = fig.colorbar(cax)
     colorbar.ax.tick_params(labelsize=25)
     colorbar.set_label('Hz', fontsize=25)
  
     # Save the image as a PNG file
     plt.savefig(output_name, bbox_inches='tight')
     plt.close()
  