def correlations(signal, signal_weight, background, background_weight, fields, category, output_suffix=''): names = [ VARIABLES[field]['title'] if field in VARIABLES else field for field in fields ] # draw correlation plots plot_corrcoef_matrix(signal, fields=names, output_name=os.path.join( PLOTS_DIR, "correlation_signal_%s%s.png" % (category.name, output_suffix)), title='%s Signal' % category.label, weights=signal_weight) plot_corrcoef_matrix(background, fields=names, output_name=os.path.join( PLOTS_DIR, "correlation_background_%s%s.png" % (category.name, output_suffix)), title='%s Background' % category.label, weights=background_weight)
def correlations(signal, signal_weight, background, background_weight, fields, category, output_suffix=""): names = [VARIABLES[field]["title"] if field in VARIABLES else field for field in fields] # draw correlation plots plot_corrcoef_matrix( signal, fields=names, output_name=os.path.join(PLOTS_DIR, "correlation_signal_%s%s.png" % (category.name, output_suffix)), title="%s Signal" % category.label, weights=signal_weight, ) plot_corrcoef_matrix( background, fields=names, output_name=os.path.join(PLOTS_DIR, "correlation_background_%s%s.png" % (category.name, output_suffix)), title="%s Background" % category.label, weights=background_weight, )
import string from rootpy.plotting.contrib import plot_corrcoef_matrix if __name__ == '__main__': import numpy as np n_vars = 10 var_names = ['var_%s' % s for s in string.lowercase[:n_vars]] def random_symm(n): a = np.random.random_integers(-10, 10, size=(n, n)) return (a + a.T) / 2 data = np.random.multivariate_normal(-np.random.random(n_vars) * 3, cov=random_symm(n_vars), size=100000) weights = np.random.randint(1, 10, 100000) plot_corrcoef_matrix(data, var_names, 'correlations.png', weights=weights, title='correlations')
import string from rootpy.plotting.contrib import plot_corrcoef_matrix if __name__ == '__main__': import numpy as np n_vars = 10 var_names = ['var_%s' % s for s in string.lowercase[:n_vars]] def random_symm(n): a = np.random.random_integers(-10, 10, size=(n, n)) return (a + a.T) / 2 data = np.random.multivariate_normal( -np.random.random(n_vars) * 3, cov=random_symm(n_vars), size=100000) weights = np.random.randint(1, 10, 100000) plot_corrcoef_matrix( data, var_names, 'correlations.png', weights=weights, title='correlations')