def neg(x):
'''Returns the negitive of a Measurement object
'''
import qexpy.error as e
x, = check_values(x)
result_derivative = {}
for key in x.derivative:
result_derivative[key] = -x.derivative[key]
result = e.Function(-x.mean, x.std)
result.derivative.update(result_derivative)
result._update_info('neg', x, func_flag=1)
result.error_flag = True
return result
def operation_wrap(operation, *args, func_flag=False):
'''e.Function wrapper to convert existing, constant functions into functions
which can handle measurement objects and return an error propagated by
derivative, min-max, or Monte Carlo method.
'''
import qexpy.error as e
args = check_values(*args)
if len(args) > 1:
args[0].get_covariance(args[1])
if func_flag is False:
args[0]._check_der(args[1])
args[1]._check_der(args[0])
df = {}
for key in args[0].derivative:
df[key] = diff[operation](key, *args)
if check_formula(operation, *args, func_flag=func_flag) is not None:
return check_formula(op_string[operation], *args, func_flag=func_flag)
mean = operation(*args)
std = dev(*args, der=df)
result = e.Function(mean, std)
result.der = [mean, std]
result.MinMax = find_minmax(operation, *args)
result.MC, result.MC_list = monte_carlo(operation, *args)
#TODO: This is wrong: should not keep changing the mean and std
# the error method should only change this on a print!!!
# Derivative Method
#if e.ExperimentalValue.error_method == "Derivative":
# pass
# By Min-Max method
#elif e.ExperimentalValue.error_method == "Min Max":
# (mean, std, ) = result.MinMax
# Monte Carlo Method
#elif e.ExperimentalValue.error_method == 'Monte Carlo':
# (mean, std, ) = result.MC
#else:
# print('''Error method not properly set, please set to derivatie, Monte
# Carlo, or Min-Max. Derivative method used by default.''')
if func_flag is False and args[0].info["Data"] is not None\
and args[1].info['Data'] is not None\
and args[0].info['Data'].size == args[1].info['Data'].size:
d1 = args[0].info["Data"]
d2 = args[1].info["Data"]
result.info['Data'] = np.ndarray(d1.size, dtype=type(d1[0]))
for i in range(d1.size):
result.info["Data"][i] = operation(d1[i],d2[i])
elif args[0].info["Data"] is not None and func_flag is True:
result.info["Data"] = (operation(args[0].info["Data"]))
result.derivative.update(df)
result._update_info(operation, *args, func_flag=func_flag)
for root in result.root:
result.get_covariance(e.ExperimentalValue.register[root])
return result