def load_fit_result(fit_result, ws):
''' Load the fit result values into the appropriate variables '''
for var in roofit.iter_collection(ws.allVars()):
name = var.GetName()
if fit_result.floatParsFinal().find(name):
value = fit_result.floatParsFinal().find(name).getVal()
if math.isnan(value):
log.error("Var %s is NAN - returning false!", name)
return False
var.setVal(value)
log.info("Setting var %s to %f", name, value)
return True
x = ws.var('x')
cut = ws.cat('cut')
# Put everything in python form
data = {}
fake_rate_datas = ws.allData()
# Plot the results of each one
for datum in fake_rate_datas:
data[datum.GetName()] = datum
print datum
functions = {}
log.info("Loading functions")
# We need to unmangle the function names
name_extractor = re.compile('func_(?P<name>.*)')
for function in roofit.iter_collection(ws.allFunctions()):
function_name = function.GetName()
log.info("Found function: %s", function_name)
match = name_extractor.match(function_name)
if not match:
log.warning("Can't find name for function, skipping!")
continue
functions[match.group('name')] = function
log.info("Added function with name %s", match.group('name'))
canvas = ROOT.TCanvas("basdf", "aasdf", 800, 600)
keeps = []
frame = x.frame()
# Keep track of output files we write
output_files = []
