def test_legacy_code():
"Test of legacy functions"
arr_def = ([1, 2], [0.1, 0.01])
# uarray, nominal_values, std_devs from the uncertainties module
# (not the uncertainties.unumpy module):
arr = uncertainties.array_u(arr_def) # Warning is normal
assert numpy.all(uncertainties.nominal_values(arr) == arr_def[0]) # Warning is normal
assert numpy.all(uncertainties.std_devs(arr) == arr_def[1]) # Warning is normal
def ratio13 ( mx, mxErr, lag=0, dnu_mean=0):
"""
Given a frequency matrix and errors calculates the ratio r13
and propagates errors.
"""
(Nn, Nl) = np.shape(mx)
r13 = np.zeros((1,Nn-1))
r13Err = np.zeros((1,Nn-1))
r13.fill(None) # values that can't be calculated are NaN
for n in range(0,Nn-1):
if (mx[n,1] and mx[n-1+lag,3] and mx[n+1,0] and mx[n,0]
and (mx[n+1,0]-mx[n,0] < 1.5*dnu_mean) ):
a = un.ufloat( (mx[n,1], mxErr[n,1]) )
b = un.ufloat( (mx[n-1+lag,3], mxErr[n-1+lag,3]) )
c = un.ufloat( (mx[n+1,0], mxErr[n+1,0]) )
d = un.ufloat( (mx[n,0], mxErr[n,0]) )
result = (a-b) / (10.*(c-d))
r13[0,n] = un.nominal_values(result)
r13Err[0,n] = un.std_dev(result)
return r13, r13Err
def ratio02 ( mx , mxErr, lag02=0, lag01=0, dnu_mean=0):
"""
Given a frequency matrix and errors calculates the ratio r02
and propagates errors.
"""
(Nn, Nl) = np.shape(mx)
r02 = np.zeros((1,Nn-1))
r02Err = np.zeros((1,Nn-1))
r02.fill(None) # values that can't be calculated are NaN
for n in range(0,Nn-1):
if (mx[n,0] != 0. and mx[n-1+lag02,2] != 0. and mx[n,1] != 0. and mx[n-1,1] != 0.
and n-1 >= 0 and n-1+lag02 >= 0
and (mx[n,1]-mx[n-1,1] < 1.5*dnu_mean)):
a = un.ufloat( (mx[n,0], mxErr[n,0]) )
b = un.ufloat( (mx[n-1+lag02,2], mxErr[n-1+lag02,2]) )
c = un.ufloat( (mx[n,1], mxErr[n,1]) )
d = un.ufloat( (mx[n-1,1], mxErr[n-1,1]) )
result = (a-b) / (6.*(c-d))
r02[0,n] = un.nominal_values(result)
r02Err[0,n] = un.std_dev(result)
return r02, r02Err
