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
'''test code for inverse and least squares with the uncertainties package''' import uncertainties as u import numpy as np a = np.array([[2, 1], [3, -1]]) b = np.array([9, 16]) x = np.linalg.solve(a, b) print "numpy solve", x x = u.numpy.linalg.solve(a, b) print "unc solve", x x = np.dot(np.linalg.inv(a), b) print "numpy inv", x x = u.numpy.dot(u.numpy.linalg.inv(a), b) print "unc inv", x x = np.linalg.lstsq(a, b)[0] print "numpy lstsq", x x = u.numpy.linalg.lstsq(a, b)[0] print "unc lstsq", x print "now with uncertainties!" au = np.matrix(u.array_u(([[2, 1], [3, -1]], [[0.2, 0.1], [0.3, 0.1]]))) bu = u.array_u(([9, 16], [0.1, 0.1])) xu = np.dot(au.I, bu) print "numpy inv", xu # Prints the result with uncertainties! xu_lst = np.dot(np.dot(np.dot(au.T, au).I, au.T), bu) print xu_lst
