sample.name,
fisher_directory,
'eigenvector',
args.selection,
WC_string,
'_'.join(inputvariables) if len([k for k, j in zip(inputvariables, w.variables) if k != j]) > 0 else 'all',
'full' if not args.fpsScaling else 'fps',
)
if not os.path.isdir(plot_directory_): os.makedirs(plot_directory_)
if os.path.isfile(os.path.join(plot_directory_, 'ev_file.log')):
os.remove(os.path.join(plot_directory_, 'ev_file.log'))
# Fill dictionaries with Eigenvalues and Eigenvectors
for i, item in enumerate(data):
print_matrix = w.matrix_to_string(*w.get_total_fisherInformation_matrix(
item['coeff'], inputvariables, **WC))
evs, evecs = np.linalg.eigh(
w.get_total_fisherInformation_matrix(item['coeff'], inputvariables,
**WC)[1])
evecs_frac = [[abs(entry) / sum(abs(vec)) for entry in vec]
for vec in evecs.T]
item['x_pos'] = i + 1
item['evs'] = evs
item['evecs_frac'] = evecs_frac
with open(os.path.join(plot_directory_, 'ev_file.log'), 'a') as f:
f.write('bin %i\n' % i)
f.write('matrix\n')
f.write(print_matrix)
f.write('\neval\n')
weightFunction=weightFunction)
full['color'] = 15
expo = 1. / len(args.variables)
data = [full] if not args.fpsScaling else []
data += selections + plotVariables2D + plotVariables3D + plotVariables4D
n_data = len(data)
# Fill dictionaries with normalized fisher information and plotting data
for i, item in enumerate(data):
# if number of events per bin < args.nEventThresh for all bins, the list will be empty
if len(item['coeff']) == 0:
norm_detI = 0
else:
detI = np.linalg.det(
w.get_total_fisherInformation_matrix(item['coeff'], args.variables,
**WC)[1])
if i == 0: detI0 = detI
norm_detI = abs(detI / detI0)**expo if detI0 != 0 else 0
item['x_graph'] = array('d', range(1, n_data + 1))
item['y_graph'] = array('d', [0] * i + [norm_detI] + [0] * (n_data - i))
# Plots
def drawPlot(log=False):
''' Plotting function
'''
scalingLabel = 'full' if not args.fpsScaling else 'fps'
def getTGraph(n, x, y, color=40):
''' Create a TGraph object
